subject:(Modeling Control AND Learning for Soft Robots)

Georgia Tech

1. Wang, Sen. Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model.

Degree: MS, Electrical and Computer Engineering, 2020, Georgia Tech

► Chinese calligraphy is a unique art form with great artistic value but difficult to master. In this thesis, we formulate the calligraphy writing problem as… (more)

Subjects/Keywords: Motion and Path Planning; Optimization and Optimal Control; Modeling, Control, and Learning for Soft Robots

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APA (6^th Edition):

Wang, S. (2020). Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64143

Chicago Manual of Style (16^th Edition):

Wang, Sen. “Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model.” 2020. Masters Thesis, Georgia Tech. Accessed April 15, 2021. http://hdl.handle.net/1853/64143.

MLA Handbook (7^th Edition):

Wang, Sen. “Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model.” 2020. Web. 15 Apr 2021.

Vancouver:

Wang S. Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model. [Internet] [Masters thesis]. Georgia Tech; 2020. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/1853/64143.

Council of Science Editors:

Wang S. Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model. [Masters Thesis]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64143

Colorado State University

2. Pawlowski, Ben. Modeling, simulation, and control of soft robots.

Degree: MS(M.S.), Mechanical Engineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/199791

► Soft robots are a new type of robot with deformable bodies and muscle-like actuations, which are fundamentally different from traditional robots with rigid links and… (more)

Subjects/Keywords: reinforcement learning; symplectic integration; soft robots; model predictive control

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APA (6^th Edition):

Pawlowski, B. (2019). Modeling, simulation, and control of soft robots. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/199791

Chicago Manual of Style (16^th Edition):

Pawlowski, Ben. “Modeling, simulation, and control of soft robots.” 2019. Masters Thesis, Colorado State University. Accessed April 15, 2021. http://hdl.handle.net/10217/199791.

MLA Handbook (7^th Edition):

Pawlowski, Ben. “Modeling, simulation, and control of soft robots.” 2019. Web. 15 Apr 2021.

Vancouver:

Pawlowski B. Modeling, simulation, and control of soft robots. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10217/199791.

Council of Science Editors:

Pawlowski B. Modeling, simulation, and control of soft robots. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/199791

3. Kennedy, Monroe David. Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation.

Degree: 2019, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/3299

► As advances are made in robotic hardware, the complexity of tasks they are capable of performing also increases. One goal of modern robotics is to… (more)

▼ As advances are made in robotic hardware, the complexity of tasks they are capable of performing also increases. One goal of modern robotics is to introduce robotic platforms that require very little augmentation of their environments to be effective and robust. Therefore the challenge for a roboticist is to develop algorithms and control strategies that leverage knowledge of the task while retaining the ability to be adaptive, adjusting to perturbations in the environment and task assumptions. This work considers approaches to these challenges in the context of a wet-lab robotic assistant. The tasks considered are cooperative transport with limited communication between team members, and robot-assisted rapid experiment preparation requiring pouring reagents from open containers useful for research and development scientists. For cooperative transport, robots must be able to plan collision-free trajectories and agree on a final destination to minimize internal forces on the carried load. Robot teammates are considered, where robots must reach consensus to minimize internal forces. The case of a human leader, and robot follower is then considered, where robots must use non-verbal information to estimate the human leader's intended pose for the carried load. For experiment preparation, the robot must pour precisely from open containers with known fluid in a single attempt. Two scenarios examined are when the geometries of the pouring and receiving containers and behaviors are known, and when the pourer must be approximated. An analytical solution is presented for a given geometry in the first instance. In the second instance, a combination of online system identification and leveraging of model priors is used to achieve the precision-pour in a single attempt with considerations for long-term robot deployment. The main contributions of this work are considerations and implementations for making robots capable of performing complex tasks with an emphasis on combining model-based and data-driven approaches for best performance.

Subjects/Keywords: Manipulation Planning; Model Learning for Control; Motion Control; Service Robots; Mechanical Engineering; Robotics

13 more images…

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APA (6^th Edition):

Kennedy, M. D. (2019). Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3299

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kennedy, Monroe David. “Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation.” 2019. Thesis, University of Pennsylvania. Accessed April 15, 2021. https://repository.upenn.edu/edissertations/3299.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kennedy, Monroe David. “Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation.” 2019. Web. 15 Apr 2021.

Vancouver:

Kennedy MD. Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2021 Apr 15]. Available from: https://repository.upenn.edu/edissertations/3299.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kennedy MD. Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3299

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

4. Havens, Aaron. Model-based approaches for learning control from multi-modal data.

Degree: MS, Aerospace Engineering, 2020, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/108550

► Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has… (more)

Subjects/Keywords: model predictive control; reinforcement learning; soft robotics

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APA (6^th Edition):

Havens, A. (2020). Model-based approaches for learning control from multi-modal data. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/108550

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Havens, Aaron. “Model-based approaches for learning control from multi-modal data.” 2020. Thesis, University of Illinois – Urbana-Champaign. Accessed April 15, 2021. http://hdl.handle.net/2142/108550.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Havens, Aaron. “Model-based approaches for learning control from multi-modal data.” 2020. Web. 15 Apr 2021.

Vancouver:

Havens A. Model-based approaches for learning control from multi-modal data. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/2142/108550.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Havens A. Model-based approaches for learning control from multi-modal data. [Thesis]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/108550

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Nguyen, Tuan. Learning Control of Closed-Kinematic Chain Mechanism Manipulators.

Degree: 2020, The Catholic University of America

URL: http://hdl.handle.net/1961/cuislandora:214714

►

Human beings possess the ability to learn from experience from their repeated activities. For example, professional athletes exercise repeatedly to acquire an ideal form of… (more)

▼

Human beings possess the ability to learn from experience from their repeated activities. For example, professional athletes exercise repeatedly to acquire an ideal form of motion, e.g. their serves in tennis or their free kicks in soccer, etc. Researchers have wondered if robots are able to learn automatically from previous tasks and improve their performance at the next trial. To perform repeatable tasks, robot manipulators can be taught off-line by a so-called teaching and playback scheme. To improve repeatable tasks, an online learning process is based on iteration rules that generate a current actuator input which is better than the previous one under the condition that a desired output is specified. This dissertation presents a trajectory control scheme whose development is based on iterative learning theory, for a class of robot manipulators with closed-kinematic chain mechanism (CKCM). The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system consists of a Proportional-Integral-Derivative (PID) feedback servo with a model-based feedforward compensation. The learning control system takes on the form of a PID-type learning to provide additional inputs to improve the robot performance after each trial. The stability of thePID-type learning law is established with a gain design procedure. The proposed control scheme will be developed and evaluated by computer simulation and experimentation. In particular, the developed control scheme will be applied to a simulation model of a six degree-of-freedom (DOF) CKCM manipulator and a two DOF planar CKCM manipulator whose kinematics and dynamics will be developed for computer simulation. The optimized gains of the control scheme will then be applied to the above two CKCM manipulators for experimental evaluation. Finally, comparative performance evaluation of the developed control scheme will be conducted with respect to other existing learning control schemes and its superior performance manifests its contribution to the state-of-the-art of learning control of CKCM manipulators.

Robotics

Engineering

CKCM Manipulators, Iterative Learning Control, Learning control, Parallel robots, Robot Manipulators

Electrical Engineering and Computer Science

Degree Awarded: Ph.D. Electrical Engineering and Computer Science. The Catholic University of America

Advisors/Committee Members: The Catholic University of America (Degree granting institution), Nguyen, Charles (Thesis advisor), Plaku, Erion (Committee member), Lum, Peter (Committee member).

Subjects/Keywords: CKCM Manipulators; Iterative Learning Control; Learning control; Parallel robots; Robot Manipulators

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APA (6^th Edition):

Nguyen, T. (2020). Learning Control of Closed-Kinematic Chain Mechanism Manipulators. (Thesis). The Catholic University of America. Retrieved from http://hdl.handle.net/1961/cuislandora:214714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nguyen, Tuan. “Learning Control of Closed-Kinematic Chain Mechanism Manipulators.” 2020. Thesis, The Catholic University of America. Accessed April 15, 2021. http://hdl.handle.net/1961/cuislandora:214714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nguyen, Tuan. “Learning Control of Closed-Kinematic Chain Mechanism Manipulators.” 2020. Web. 15 Apr 2021.

Vancouver:

Nguyen T. Learning Control of Closed-Kinematic Chain Mechanism Manipulators. [Internet] [Thesis]. The Catholic University of America; 2020. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/1961/cuislandora:214714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nguyen T. Learning Control of Closed-Kinematic Chain Mechanism Manipulators. [Thesis]. The Catholic University of America; 2020. Available from: http://hdl.handle.net/1961/cuislandora:214714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Hong Kong University of Science and Technology

6. Li, Siyi CSE. Learning perception and control for robot intelligence.

Degree: 2019, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-100352 ; https://doi.org/10.14711/thesis-991012730762103412 ; http://repository.ust.hk/ir/bitstream/1783.1-100352/1/th_redirect.html

► Autonomous robots that can assist humans in the daily unstructured world have been a long standing vision of robotics and artificial intelligence (AI). Such autonomous… (more)

▼ Autonomous robots that can assist humans in the daily unstructured world have been a long standing vision of robotics and artificial intelligence (AI). Such autonomous intelligent robotic system requires two essential building blocks: perception and control. Meanwhile, the past few years have seen major advances in many perception and control tasks empowered by deep learning and reinforcement learning methods. Hence one natural question to ask is how AI techniques could help to accomplish those robotic tasks. In this thesis, we explore learning-based solutions to robotic tasks. Our first attempt is constructing a unified benchmark for visual object tracking on the unmanned aerial vehicle (UAV) platform. We manually built a drone tracking dataset, consisting of a variety of videos with high diversity captured by drone cameras. We performed an extensive empirical study of the state-of-the-art methods on the dataset and identified their major weakness in the motion model. We also devised new motion models by explicitly estimating the camera motion in the tracking phase, which are especially suitable and effective for the drone tracking scenario. Collecting real-world data with robotic systems is generally expensive due to the hardware cost and the manual labeling effort. However, deep learning and reinforcement learning methods require a data-hungry training paradigm. We proposed to address this issue by learning from synthetic data while minimizing the gap from simulation to reality at the same time. For robotic perception task, we investigated instance segmentation for robot manipulation. We developed an automated rendering pipeline to generate a variety of photorealistic synthetic images with pixel-level labels. The synthetic dataset is then used to train an objectness deep neural network model which can successfully generalize to real-world manipulation scenarios. For robotic control task, we focused on the challenging problem of learning UAV control for actively tracking a moving target. We proposed a hierarchical approach that combines model-free reinforcement learning methods with conventional feedback controllers to enable efficient and safe exploration in the training phase. We showed that this hierarchical control scheme can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to real-world quadrotor control.

Subjects/Keywords: Autonomous robots ; Mathematical models ; Robots ; Control systems ; Machine learning ; Artificial intelligence ; Perception

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APA (6^th Edition):

Li, S. C. (2019). Learning perception and control for robot intelligence. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-100352 ; https://doi.org/10.14711/thesis-991012730762103412 ; http://repository.ust.hk/ir/bitstream/1783.1-100352/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Siyi CSE. “Learning perception and control for robot intelligence.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed April 15, 2021. http://repository.ust.hk/ir/Record/1783.1-100352 ; https://doi.org/10.14711/thesis-991012730762103412 ; http://repository.ust.hk/ir/bitstream/1783.1-100352/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Siyi CSE. “Learning perception and control for robot intelligence.” 2019. Web. 15 Apr 2021.

Vancouver:

Li SC. Learning perception and control for robot intelligence. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2021 Apr 15]. Available from: http://repository.ust.hk/ir/Record/1783.1-100352 ; https://doi.org/10.14711/thesis-991012730762103412 ; http://repository.ust.hk/ir/bitstream/1783.1-100352/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li SC. Learning perception and control for robot intelligence. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-100352 ; https://doi.org/10.14711/thesis-991012730762103412 ; http://repository.ust.hk/ir/bitstream/1783.1-100352/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

7. XU WENJUN. MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES.

Degree: 2015, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/152768

Subjects/Keywords: flexible surgical manipulator; data-driven methods; reinforcement learning; learn from demonstration; learning control; soft robots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

WENJUN, X. (2015). MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/152768

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

WENJUN, XU. “MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES.” 2015. Thesis, National University of Singapore. Accessed April 15, 2021. http://scholarbank.nus.edu.sg/handle/10635/152768.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

WENJUN, XU. “MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES.” 2015. Web. 15 Apr 2021.

Vancouver:

WENJUN X. MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES. [Internet] [Thesis]. National University of Singapore; 2015. [cited 2021 Apr 15]. Available from: http://scholarbank.nus.edu.sg/handle/10635/152768.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

WENJUN X. MODELING AND TASK AUTOMATION FOR FLEXIBLE SURGICAL MANIPULATORS VIA DATA-DRIVEN APPROACHES. [Thesis]. National University of Singapore; 2015. Available from: http://scholarbank.nus.edu.sg/handle/10635/152768

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

8. Tsang, Florence. Learning a Motion Policy to Navigate Environments with Structured Uncertainty.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/15562

► Navigating in uncertain environments is a fundamental ability that robots must have in many applications such as moving goods in a warehouse or transporting materials… (more)

Subjects/Keywords: robot navigation; reinforcement learning; motion planning; Robots – Motion; Uncertainty (Information theory); Robots – Programming; Robots – Control systems.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tsang, F. (2020). Learning a Motion Policy to Navigate Environments with Structured Uncertainty. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15562

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tsang, Florence. “Learning a Motion Policy to Navigate Environments with Structured Uncertainty.” 2020. Thesis, University of Waterloo. Accessed April 15, 2021. http://hdl.handle.net/10012/15562.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tsang, Florence. “Learning a Motion Policy to Navigate Environments with Structured Uncertainty.” 2020. Web. 15 Apr 2021.

Vancouver:

Tsang F. Learning a Motion Policy to Navigate Environments with Structured Uncertainty. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10012/15562.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tsang F. Learning a Motion Policy to Navigate Environments with Structured Uncertainty. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15562

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Geisert, Mathieu. Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens.

Degree: Docteur es, Robotique, 2018, Toulouse, INSA

URL: http://www.theses.fr/2018ISAT0011

►

Quelle sont les points communs entre un robot humanoïde et un quadrimoteur ? Et bien, pas grand-chose… Cette thèse est donc dédiée au développement d’algorithmes… (more)

▼

Quelle sont les points communs entre un robot humanoïde et un quadrimoteur ? Et bien, pas grand-chose… Cette thèse est donc dédiée au développement d’algorithmes permettant de contrôler un robot de manière dynamique tout en restant générique par rapport au model du robot et à la tâche que l’on cherche à résoudre. Le contrôle optimal numérique est pour cela un bon candidat. Cependant il souffre de plusieurs difficultés comme un nombre important de paramètres à ajuster et des temps de calcul relativement élevés. Ce document présente alors plusieurs améliorations permettant d’atténuer ces difficultés. D’un côté, l’ordonnancement des différentes tâches sous la forme d’une hiérarchie et sa résolution avec un algorithme adapté permet de réduire le nombre de paramètres à ajuster. D’un autre côté, l’utilisation de l’apprentissage automatique afin d’initialiser l’algorithme d’optimisation ou de générer un modèle simplifié du robot permet de fortement diminuer les temps de calcul.

What are the common characteristics of humanoid robots and quadrotors? Well, not many… Therefore, this thesis focuses on the development of algorithms allowing to dynamically control a robot while staying generic with respect to the model of the robot and the task that needs to be solved. Numerical optimal control is good candidate to achieve such objective. However, it suffers from several difficulties such as a high number of parameters to tune and a relatively important computation time. This document presents several ameliorations allowing to reduce these problems. On one hand, the tasks can be ordered according to a hierarchy and solved with an appropriate algorithm to lower the number of parameters to tune. On the other hand, machine learning can be used to initialize the optimization solver or to generate a simplified model of the robot, and therefore can be used to decrease the computation time.

Advisors/Committee Members: Mansard, Nicolas (thesis director).

Subjects/Keywords: Contrôle optimal numérique; Contrôle hiérarchique; Apprentissage automatique; Planification de contacts; Robots humanoïdes; Robots aériens; Numerical optimal control; Machine learning; Machine learning; Contact planning; Humanoid robots; Aerial robots; 629.8

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Geisert, M. (2018). Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens. (Doctoral Dissertation). Toulouse, INSA. Retrieved from http://www.theses.fr/2018ISAT0011

Chicago Manual of Style (16^th Edition):

Geisert, Mathieu. “Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens.” 2018. Doctoral Dissertation, Toulouse, INSA. Accessed April 15, 2021. http://www.theses.fr/2018ISAT0011.

MLA Handbook (7^th Edition):

Geisert, Mathieu. “Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens.” 2018. Web. 15 Apr 2021.

Vancouver:

Geisert M. Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens. [Internet] [Doctoral dissertation]. Toulouse, INSA; 2018. [cited 2021 Apr 15]. Available from: http://www.theses.fr/2018ISAT0011.

Council of Science Editors:

Geisert M. Optimal control and machine learning for humanoid and aerial robots : Contrôle optimal et apprentissage automatique pour robots humanoïdes et aériens. [Doctoral Dissertation]. Toulouse, INSA; 2018. Available from: http://www.theses.fr/2018ISAT0011

Delft University of Technology

10. van Lohuijzen, Michiel (author). Towards incremental kinesthetic teaching of bipedal walking.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

► The large dimensionality of walking motions is a challenge for robot learning. The human seems designated to assist in this learning process, because of their… (more)

Subjects/Keywords: Kinesthetic Teaching; Iterative Learning Control; Bipedal Walking; Learning from Demonstrations; Learning by Imitation; Robots

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APA (6^th Edition):

van Lohuijzen, M. (. (2017). Towards incremental kinesthetic teaching of bipedal walking. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

Chicago Manual of Style (16^th Edition):

van Lohuijzen, Michiel (author). “Towards incremental kinesthetic teaching of bipedal walking.” 2017. Masters Thesis, Delft University of Technology. Accessed April 15, 2021. http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a.

MLA Handbook (7^th Edition):

van Lohuijzen, Michiel (author). “Towards incremental kinesthetic teaching of bipedal walking.” 2017. Web. 15 Apr 2021.

Vancouver:

van Lohuijzen M(. Towards incremental kinesthetic teaching of bipedal walking. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Apr 15]. Available from: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a.

Council of Science Editors:

van Lohuijzen M(. Towards incremental kinesthetic teaching of bipedal walking. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

University of Plymouth

11. Loviken, Pontus. Fast online model learning for controlling complex real-world robots.

Degree: PhD, 2019, University of Plymouth

URL: http://hdl.handle.net/10026.1/15078

► How can real robots with many degrees of freedom - without previous knowledge of themselves or their environment - act and use the resulting observations… (more)

▼ How can real robots with many degrees of freedom - without previous knowledge of themselves or their environment - act and use the resulting observations to efficiently develop the ability to generate a wide set of useful behaviours? This thesis presents a novel framework that enables physical robots with many degrees of freedom to rapidly learn models for control from scratch. This can be done in previously inaccessible problem domains characterised by a lack of direct mappings from motor actions to outcomes, as well as state and action spaces too large for the full forward dynamics to be learned and used explicitly. The proposed framework is able to cope with these issues by the use of a set of local Goal Babbling models, that maps every outcome in a low dimensional task space to a specific action, together with a sparse higher level Reinforcement Learning model, that learns to navigate between the contexts from which each Goal Babbling model can be used. The two types of models can then be learned online an in parallel, using only the data a robot can collect by interacting with its environment. To show the potential of the approach we present two possible implementations of the framework, over two separate robot platforms: a simulated planar arm with up to 1, 000 degrees of freedom, and a real humanoid robot with 25 degrees of freedom. The results show that learning is rapid and essentially unaffected by the number of degrees of freedom of the robot, allowing for the generation of complex behaviours and skills after a relatively short training time. The planar arm is able to strategically plan series of motions in order to move its end-effector between any two parts of a crowded environment, within 10, 000 iterations. The humanoid robot is able to freely transition between states such as lying on the back, belly, and sides, and occasionally also sitting up, within only 1, 000 iterations. This corresponds to 30 − 60 minutes of real-world interactions. The main contribution of this thesis is to provide a framework for solving a control learning problem, previously largely unexplored with no obvious solutions, but with strong analogies to, for example, early learning of body orientation control in infants. This thesis examined two quite different implementations of the proposed framework, and showed success in both cases for two different control learning problem.

Subjects/Keywords: model learning; Reinforcement learning; Online learning; Goal babbling; inverse models; Micro data learning; Developmental robotics; real-world robots; sensorimotor control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Loviken, P. (2019). Fast online model learning for controlling complex real-world robots. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/15078

Chicago Manual of Style (16^th Edition):

Loviken, Pontus. “Fast online model learning for controlling complex real-world robots.” 2019. Doctoral Dissertation, University of Plymouth. Accessed April 15, 2021. http://hdl.handle.net/10026.1/15078.

MLA Handbook (7^th Edition):

Loviken, Pontus. “Fast online model learning for controlling complex real-world robots.” 2019. Web. 15 Apr 2021.

Vancouver:

Loviken P. Fast online model learning for controlling complex real-world robots. [Internet] [Doctoral dissertation]. University of Plymouth; 2019. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10026.1/15078.

Council of Science Editors:

Loviken P. Fast online model learning for controlling complex real-world robots. [Doctoral Dissertation]. University of Plymouth; 2019. Available from: http://hdl.handle.net/10026.1/15078

University of Illinois – Chicago

12. Monfort, Mathew. Methods in Large Scale Inverse Optimal Control.

Degree: 2016, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/21540

► As our technology continues to evolve, so does the complexity of the problems that we expect our systems to solve. The challenge is that these… (more)

Subjects/Keywords: machine learning; artificial intelligence; inverse optimal control; graph search; autonomous agents; reinforcement learning; path distributions; robotic control; robotics; robots; activity recognition

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APA (6^th Edition):

Monfort, M. (2016). Methods in Large Scale Inverse Optimal Control. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21540

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Monfort, Mathew. “Methods in Large Scale Inverse Optimal Control.” 2016. Thesis, University of Illinois – Chicago. Accessed April 15, 2021. http://hdl.handle.net/10027/21540.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Monfort, Mathew. “Methods in Large Scale Inverse Optimal Control.” 2016. Web. 15 Apr 2021.

Vancouver:

Monfort M. Methods in Large Scale Inverse Optimal Control. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10027/21540.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Monfort M. Methods in Large Scale Inverse Optimal Control. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/21540

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universitat Politècnica de València

13. Solanes Galbis, Juan Ernesto. MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL .

Degree: 2015, Universitat Politècnica de València

URL: http://hdl.handle.net/10251/57951

► [EN] This thesis deals with two characteristic problems in visual feedback robot control: 1) sensor latency; 2) providing suitable trajectories for the robot and for… (more)

▼ [EN] This thesis deals with two characteristic problems in visual feedback robot control: 1) sensor latency; 2) providing suitable trajectories for the robot and for the measurement in the image. All the approaches presented in this work are analyzed and implemented on a 6 DOF industrial robot manipulator or/and a wheeled robot. Focusing on the sensor latency problem, this thesis proposes the use of dual-rate high order holds within the control loop of robots. In this sense, the main contributions are: - Dual-rate high order holds based on primitive functions for robot control (Chapter 3): analysis of the system performance with and without the use of this multi-rate technique from non-conventional control. In addition, as consequence of the use of dual-rate holds, this work obtains and validates multi-rate controllers, especially dual-rate PIDs. - Asynchronous dual-rate high order holds based on primitive functions with time delay compensation (Chapter 3): generalization of asynchronous dual-rate high order holds incorporating an input signal time delay compensation component, improving thus the inter-sampling estimations computed by the hold. It is provided an analysis of the properties of such dual-rate holds with time delay compensation, comparing them with estimations obtained by the equivalent dual-rate holds without this compensation, as well as their implementation and validation within the control loop of a 6 DOF industrial robot manipulator. - Multi-rate nonlinear high order holds (Chapter 4): generalization of the concept of dual-rate high order holds with nonlinear estimation models, which include information about the plant to be controlled, the controller(s) and sensor(s) used, obtained from machine learning techniques. Thus, in order to obtain such a nonlinear hold, it is described a methodology non dependent of the machine technique used, although validated using artificial neural networks. Finally, an analysis of the properties of these new holds is carried out, comparing them with their equivalents based on primitive functions, as well as their implementation and validation within the control loop of an industrial robot manipulator and a wheeled robot. With respect to the problem of providing suitable trajectories for the robot and for the measurement in the image, this thesis presents the novel reference features filtering control strategy and its generalization from a multi-rate point of view. The main contributions in this regard are: - Reference features filtering control strategy (Chapter 5): a new control strategy is proposed to enlarge significantly the solution task reachability of robot visual feedback control. The main idea is to use optimal trajectories proposed by a non-linear EKF predictor-smoother (ERTS), based on Rauch-Tung-Striebel (RTS) algorithm, as new feature references for an underlying visual feedback controller. In this work it is provided both the description of the implementation algorithm and its implementation and validation utilizing an industrial robot manipulator. -… Advisors/Committee Members: Armesto Ángel, Leopoldo (advisor), Tornero Montserrat, Josep (advisor).

Subjects/Keywords: Visual feedback control; visual servoing; multi-rate control; nonlinear control; industrial robot systems; wheeled robots; machine learning.

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APA (6^th Edition):

Solanes Galbis, J. E. (2015). MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL . (Doctoral Dissertation). Universitat Politècnica de València. Retrieved from http://hdl.handle.net/10251/57951

Chicago Manual of Style (16^th Edition):

Solanes Galbis, Juan Ernesto. “MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL .” 2015. Doctoral Dissertation, Universitat Politècnica de València. Accessed April 15, 2021. http://hdl.handle.net/10251/57951.

MLA Handbook (7^th Edition):

Solanes Galbis, Juan Ernesto. “MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL .” 2015. Web. 15 Apr 2021.

Vancouver:

Solanes Galbis JE. MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL . [Internet] [Doctoral dissertation]. Universitat Politècnica de València; 2015. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10251/57951.

Council of Science Editors:

Solanes Galbis JE. MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL . [Doctoral Dissertation]. Universitat Politècnica de València; 2015. Available from: http://hdl.handle.net/10251/57951

14. Provost, Jefferson, 1968-. Reinforcement learning in high-diameter, continuous environments.

Degree: PhD, Computer Sciences, 2007, University of Texas – Austin

URL: http://hdl.handle.net/2152/3263

► Many important real-world robotic tasks have high diameter, that is, their solution requires a large number of primitive actions by the robot. For example, they… (more)

▼ Many important real-world robotic tasks have high diameter, that is, their solution requires a large number of primitive actions by the robot. For example, they may require navigating to distant locations using primitive motor control commands. In addition, modern robots are endowed with rich, high-dimensional sensory systems, providing measurements of a continuous environment. Reinforcement learning (RL) has shown promise as a method for automatic learning of robot behavior, but current methods work best on lowdiameter, low-dimensional tasks. Because of this problem, the success of RL on real-world tasks still depends on human analysis of the robot, environment, and task to provide a useful set of perceptual features and an appropriate decomposition of the task into subtasks. This thesis presents Self-Organizing Distinctive-state Abstraction (SODA) as a solution to this problem. Using SODA a robot with little prior knowledge of its sensorimotor system, environment, and task can automatically reduce the effective diameter of its tasks. First it uses a self-organizing feature map to learn higher level perceptual features while exploring using primitive, local actions. Then, using the learned features as input, it learns a set of high-level actions that carry the robot between perceptually distinctive states in the environment. Experiments in two robot navigation environments demonstrate that SODA learns useful features and high-level actions, that using these new actions dramatically speeds up learning for high-diameter navigation tasks, and that the method scales to large (buildingsized) robot environments. These experiments demonstrate SODAs effectiveness as a generic learning agent for mobile robot navigation, pointing the way toward developmental robots that learn to understand themselves and their environments through experience in the world, reducing the need for human engineering for each new robotic application. Advisors/Committee Members: Kuipers, Benjamin (advisor).

Subjects/Keywords: Robots – Control systems; Machine learning

…learning, and a set of continuous perceptual features for continuous control. 3. Learn High-level… …control and causal levels of SSH provide a framework for grounding the reinforcement learning… …to distant locations using primitive motor control commands. In addition, modern robots are… …environment. Reinforcement learning (RL) has shown promise as a method for automatic… …actions, that using these new actions dramatically speeds up learning for high-diameter…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Provost, Jefferson, 1. (2007). Reinforcement learning in high-diameter, continuous environments. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3263

Chicago Manual of Style (16^th Edition):

Provost, Jefferson, 1968-. “Reinforcement learning in high-diameter, continuous environments.” 2007. Doctoral Dissertation, University of Texas – Austin. Accessed April 15, 2021. http://hdl.handle.net/2152/3263.

MLA Handbook (7^th Edition):

Provost, Jefferson, 1968-. “Reinforcement learning in high-diameter, continuous environments.” 2007. Web. 15 Apr 2021.

Vancouver:

Provost, Jefferson 1. Reinforcement learning in high-diameter, continuous environments. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2007. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/2152/3263.

Council of Science Editors:

Provost, Jefferson 1. Reinforcement learning in high-diameter, continuous environments. [Doctoral Dissertation]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3263

Louisiana State University

15. Ma, Yan. Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems.

Degree: PhD, Chemical Engineering, 2020, Louisiana State University

URL: https://digitalcommons.lsu.edu/gradschool_dissertations/5427

► This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses… (more)

▼ This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses on 3 case studies with DRL optimization control including a polymerization reaction control with deep reinforcement learning, a bioreactor optimization, and a fed-batch reaction optimization from a reactor at Dow Inc.. In the first study, a data-driven controller based on DRL is developed for a fed-batch polymerization reaction with multiple continuous manipulative variables with continuous control. The second case study is the modeling and optimization of a bioreactor. In this study, a data-driven reaction model is developed using Artificial Neural Network (ANN) to simulate the growth curve and bio-product accumulation of cyanobacteria Plectonema. Then a DRL control agent that optimizes the daily nutrient input is applied to maximize the yield of valuable bio-product C-phycocyanin. C-phycocyanin yield is increased by 52.1% compared to a control group with the same total nutrient content in experimental validation. The third case study is employing the data-driven control scheme for optimization of a reactor from Dow Inc, where a DRL-based optimization framework is established for the optimization of the Multi-Input, Multi-Output (MIMO) reaction system with reaction surrogate modeling. Yan Ma’s research overall shows promising directions for employing the emerging technologies of data-driven methods and deep learning in the field of manufacturing and biological systems. It is demonstrated that DRL is an efficient algorithm in the study of three different reaction systems with both stochastic and deterministic policies. Also, the use of data-driven models in reaction simulation also shows promising results with the non-linear nature and fast computational speed of the neural network models.

Subjects/Keywords: process control; optimization; machine learning; reinforcement learning; process modeling

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APA (6^th Edition):

Ma, Y. (2020). Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems. (Doctoral Dissertation). Louisiana State University. Retrieved from https://digitalcommons.lsu.edu/gradschool_dissertations/5427

Chicago Manual of Style (16^th Edition):

Ma, Yan. “Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems.” 2020. Doctoral Dissertation, Louisiana State University. Accessed April 15, 2021. https://digitalcommons.lsu.edu/gradschool_dissertations/5427.

MLA Handbook (7^th Edition):

Ma, Yan. “Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems.” 2020. Web. 15 Apr 2021.

Vancouver:

Ma Y. Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems. [Internet] [Doctoral dissertation]. Louisiana State University; 2020. [cited 2021 Apr 15]. Available from: https://digitalcommons.lsu.edu/gradschool_dissertations/5427.

Council of Science Editors:

Ma Y. Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems. [Doctoral Dissertation]. Louisiana State University; 2020. Available from: https://digitalcommons.lsu.edu/gradschool_dissertations/5427

Texas State University – San Marcos

16. Huff, Shelby A. TIG Welding Skill Extraction using a Machine Learning Algorithm.

Degree: MS, Manufacturing Engineering, 2017, Texas State University – San Marcos

URL: https://digital.library.txstate.edu/handle/10877/6930

► Tungsten Inert Gas (TIG) welding is the superior arc welding process used in the manufacturing industry for high quality welds. Skilled welders are capable of… (more)

Subjects/Keywords: TIG Welding; Artificial Inteligence; Machine Learning; Gaussian Process Regression; Industrial Robots; Manufacturing Engineering; Manufacturing processes; Light metals – Welding; Robots – Control systems; Machine learning; Computer algorithms

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APA (6^th Edition):

Huff, S. A. (2017). TIG Welding Skill Extraction using a Machine Learning Algorithm. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6930

Chicago Manual of Style (16^th Edition):

Huff, Shelby A. “TIG Welding Skill Extraction using a Machine Learning Algorithm.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed April 15, 2021. https://digital.library.txstate.edu/handle/10877/6930.

MLA Handbook (7^th Edition):

Huff, Shelby A. “TIG Welding Skill Extraction using a Machine Learning Algorithm.” 2017. Web. 15 Apr 2021.

Vancouver:

Huff SA. TIG Welding Skill Extraction using a Machine Learning Algorithm. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2021 Apr 15]. Available from: https://digital.library.txstate.edu/handle/10877/6930.

Council of Science Editors:

Huff SA. TIG Welding Skill Extraction using a Machine Learning Algorithm. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6930

University of Windsor

17. Mellatshahi, Seyed Navid. Learning Control of Robotic Arm Using Deep Q-Neural Network.

Degree: MS, Electrical and Computer Engineering, 2021, University of Windsor

URL: https://scholar.uwindsor.ca/etd/8568

► Enabling robotic systems for autonomous actions such as driverless systems, is a very complex task in real-world scenarios due to uncertainties. Machine learning capabilities have… (more)

Subjects/Keywords: AI in Robotic; Artificial Intelligence in Industrial Robots; Deep Reinforcement Learning; DQN; Inverted Pendulum; Learning Control

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APA (6^th Edition):

Mellatshahi, S. N. (2021). Learning Control of Robotic Arm Using Deep Q-Neural Network. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/8568

Chicago Manual of Style (16^th Edition):

Mellatshahi, Seyed Navid. “Learning Control of Robotic Arm Using Deep Q-Neural Network.” 2021. Masters Thesis, University of Windsor. Accessed April 15, 2021. https://scholar.uwindsor.ca/etd/8568.

MLA Handbook (7^th Edition):

Mellatshahi, Seyed Navid. “Learning Control of Robotic Arm Using Deep Q-Neural Network.” 2021. Web. 15 Apr 2021.

Vancouver:

Mellatshahi SN. Learning Control of Robotic Arm Using Deep Q-Neural Network. [Internet] [Masters thesis]. University of Windsor; 2021. [cited 2021 Apr 15]. Available from: https://scholar.uwindsor.ca/etd/8568.

Council of Science Editors:

Mellatshahi SN. Learning Control of Robotic Arm Using Deep Q-Neural Network. [Masters Thesis]. University of Windsor; 2021. Available from: https://scholar.uwindsor.ca/etd/8568

18. Wang, Ting. Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics.

Degree: Docteur es, Traitement du Signal et des Images, 2012, Université Paris-Est

URL: http://www.theses.fr/2012PEST1109

► L'objectif de cette thèse s'inscrit dans la cadre général du développement d'une stratégie de contrôle intelligent distribué en robotique collective. En effet, dans un avenir… (more)

▼ L'objectif de cette thèse s'inscrit dans la cadre général du développement d'une stratégie de contrôle intelligent distribué en robotique collective. En effet, dans un avenir proche, de nombreux robots vont progressivement intégrer notre environnent aussi bien dans les milieux industriel que domestique. L'objectif de ces robots sera de fournir, de manière autonome, des services aux êtres humains afin de leurs faciliter la vie quotidienne comme par exemple dans le cas de robots compagnons. Ces services pourront être le résultat du travail d'un robot ou bien la conséquence de la coopération de plusieurs robots homogènes et/ou hétérogènes regroupés au sein d'un réseau. Dans ce contexte, si les progrès technologiques permettent sans problème de communiquer et d'échanger des données entre deux agents artificiels distants, la conception de stratégies de contrôle permettant l'auto-organisation de plusieurs robots dans le but de réaliser une tâche précise est encore aujourd'hui un verrou scientifique important. Cette thèse a donc pour but de proposer des pistes pour élaborer des stratégies de contrôle intelligent pour des systèmes multi-robots dans le cadre plus particulier de la logistique industrielle. En effet, le domaine de logistique industrielle nécessite l'utilisation de nombreux robots mobiles comme par exemple des AGV (Automatic Guided Vehicles) pour transporter et stoker des marchandises. Dans ce contexte, nous pensons que le domaine de la logistique peut tirer bénéfice de l'utilisation de systèmes multi-robots. Dans un premier temps, cette thèse aborde donc la problématique de transport d'objet volumineux et encombrant par une formation de robot. Effectivement, il semble que la solution qui consiste à utiliser un ensemble de robots identiques pour transporter des charges de grandes envergures soit, d'une part, très intéressante d'un point de vue économique et, d'autre part, plus robuste et flexible d'un point vue technologique. Dans un deuxième temps, cette thèse aborde l'utilisation d'un réseau de robots hétérogènes qui sont capables de s'organiser afin de réaliser une tâche précise dans un milieu dynamique. Les travaux effectués dans le cadre de la présente thèse doctorale ont donc abouti à la proposition des stratégies viables de contrôle intelligent pour des systèmes multi-robots. Une étude d'application des concepts étudiés a été réalisée, implantée et validée dans le cadre plus particulier de la logistique industrielle. Elle a concerné d'abord le contexte d'un groupe multi-robots homogène, puis a été étendue au contexte d'un système multi-robots hétérogènes. Les points forts des travaux réalisés peuvent être résumés comme ceci :- Proposition, conception, réalisation et validation expérimentale d'une stratégie de contrôle adaptatif par l'apprentissage artificiel pour un robot non-holonomique. Quatre publications internationales ont valorisé cette partie des travaux.- Proposition, conception, réalisation et validation expérimentale d'une stratégie de contrôle hybridant la vision artificielle et l'apprentissage… Advisors/Committee Members: Madani, Kurosh (thesis director).

Subjects/Keywords: Stratégie de contrôle intelligent; Robotique collective; Formation de robots; Apprentissage artificiel; Application logistique; Intelligent Distributed Control Strategies; Collective Robotics; Robots' formation; Machine learning; Logistic application

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, T. (2012). Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2012PEST1109

Chicago Manual of Style (16^th Edition):

Wang, Ting. “Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics.” 2012. Doctoral Dissertation, Université Paris-Est. Accessed April 15, 2021. http://www.theses.fr/2012PEST1109.

MLA Handbook (7^th Edition):

Wang, Ting. “Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics.” 2012. Web. 15 Apr 2021.

Vancouver:

Wang T. Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics. [Internet] [Doctoral dissertation]. Université Paris-Est; 2012. [cited 2021 Apr 15]. Available from: http://www.theses.fr/2012PEST1109.

Council of Science Editors:

Wang T. Contribution à l’étude, la conception et la mise en oeuvre de stratégie de contrôle intelligent distribué en robotique collective : Contribution to study, dand implementation of intelligent distributed control strategies for collective robotics. [Doctoral Dissertation]. Université Paris-Est; 2012. Available from: http://www.theses.fr/2012PEST1109

Georgia Tech

19. Powers, Matthew D. Applying inter-layer conflict resolution to hybrid robot control architectures.

Degree: PhD, Computing, 2010, Georgia Tech

URL: http://hdl.handle.net/1853/33979

► In this document, we propose and examine the novel use of a learning mechanism between the reactive and deliberative layers of a hybrid robot control… (more)

▼ In this document, we propose and examine the novel use of a learning mechanism between the reactive and deliberative layers of a hybrid robot control architecture. Balancing the need to achieve complex goals and meet real-time constraints, many modern mobile robot navigation control systems make use of a hybrid deliberative-reactive architecture. In this paradigm, a high-level deliberative layer plans routes or actions toward a known goal, based on accumulated world knowledge. A low-level reactive layer selects motor commands based on current sensor data and the deliberative layer's plan. The desired system-level effect of this architecture is that the robot is able to combine complex reasoning toward global objectives with quick reaction to local constraints. Implicit in this type of architecture, is the assumption that both layers are using the same model of the robot's capabilities and constraints. It may happen, for example, due to differences in representation of the robot's kinematic constraints, that the deliberative layer creates a plan that the reactive layer cannot follow. This sort of conflict may cause a degradation in system-level performance, if not complete navigational deadlock. Traditionally, it has been the task of the robot designer to ensure that the layers operate in a compatible manner. However, this is a complex, empirical task. Working to improve system-level performance and navigational robustness, we propose introducing a learning mechanism between the reactive layer and the deliberative layer, allowing the deliberative layer to learn a model of the reactive layer's execution of its plans. First, we focus on detecting this inter-layer conflict, and acting based on a corrected model. This is demonstrated on a physical robotic platform in an unstructured outdoor environment. Next, we focus on learning a model to predict instances of inter-layer conflict, and planning to act with respect to this model. This is demonstrated using supervised learning in a physics-based simulation environment. Results and algorithms are presented. Advisors/Committee Members: Tucker Balch (Committee Chair), Anthony Stentz (Committee Member), Henrik Christensen (Committee Member), Magnus Egerstedt (Committee Member), Ronald Arkin (Committee Member).

Subjects/Keywords: Robotics; Planning; Machine learning; Architecture; Robots Control systems; Mobile robots

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APA (6^th Edition):

Powers, M. D. (2010). Applying inter-layer conflict resolution to hybrid robot control architectures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33979

Chicago Manual of Style (16^th Edition):

Powers, Matthew D. “Applying inter-layer conflict resolution to hybrid robot control architectures.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 15, 2021. http://hdl.handle.net/1853/33979.

MLA Handbook (7^th Edition):

Powers, Matthew D. “Applying inter-layer conflict resolution to hybrid robot control architectures.” 2010. Web. 15 Apr 2021.

Vancouver:

Powers MD. Applying inter-layer conflict resolution to hybrid robot control architectures. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/1853/33979.

Council of Science Editors:

Powers MD. Applying inter-layer conflict resolution to hybrid robot control architectures. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33979

Loughborough University

20. Zhao, Yuchen. Human skill capturing and modelling using wearable devices.

Degree: PhD, 2017, Loughborough University

URL: http://hdl.handle.net/2134/27613

► Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to… (more)

▼ Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to…

Subjects/Keywords: 670.42; Manufacturing automation; Force based control; Motion Capturing (MoCap); Learning from Demonstration (LfD); Surface electromyography (sEMG); Robots

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APA (6^th Edition):

Zhao, Y. (2017). Human skill capturing and modelling using wearable devices. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/27613

Chicago Manual of Style (16^th Edition):

Zhao, Yuchen. “Human skill capturing and modelling using wearable devices.” 2017. Doctoral Dissertation, Loughborough University. Accessed April 15, 2021. http://hdl.handle.net/2134/27613.

MLA Handbook (7^th Edition):

Zhao, Yuchen. “Human skill capturing and modelling using wearable devices.” 2017. Web. 15 Apr 2021.

Vancouver:

Zhao Y. Human skill capturing and modelling using wearable devices. [Internet] [Doctoral dissertation]. Loughborough University; 2017. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/2134/27613.

Council of Science Editors:

Zhao Y. Human skill capturing and modelling using wearable devices. [Doctoral Dissertation]. Loughborough University; 2017. Available from: http://hdl.handle.net/2134/27613

Michigan State University

21. Howden, Sally Jean. Towards a learning system for robot hand-eye coordination.

Degree: PhD, Department of Computer Science, 1996, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:29763

Subjects/Keywords: Machine learning; Robots – Control systems; Eye-hand coordination

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Howden, S. J. (1996). Towards a learning system for robot hand-eye coordination. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:29763

Chicago Manual of Style (16^th Edition):

Howden, Sally Jean. “Towards a learning system for robot hand-eye coordination.” 1996. Doctoral Dissertation, Michigan State University. Accessed April 15, 2021. http://etd.lib.msu.edu/islandora/object/etd:29763.

MLA Handbook (7^th Edition):

Howden, Sally Jean. “Towards a learning system for robot hand-eye coordination.” 1996. Web. 15 Apr 2021.

Vancouver:

Howden SJ. Towards a learning system for robot hand-eye coordination. [Internet] [Doctoral dissertation]. Michigan State University; 1996. [cited 2021 Apr 15]. Available from: http://etd.lib.msu.edu/islandora/object/etd:29763.

Council of Science Editors:

Howden SJ. Towards a learning system for robot hand-eye coordination. [Doctoral Dissertation]. Michigan State University; 1996. Available from: http://etd.lib.msu.edu/islandora/object/etd:29763

University of New Mexico

22. Appel, Titus. The development of a robotic test bed with applications in Q-learning.

Degree: Electrical and Computer Engineering, 2012, University of New Mexico

URL: http://hdl.handle.net/1928/17331

► In this work, we show the design, development, and testing of an autonomous ground vehicle for experiments in learning and intelligent transportation research. We then… (more)

▼ In this work, we show the design, development, and testing of an autonomous ground vehicle for experiments in learning and intelligent transportation research. We then implement the Q-Learning algorithm to teach the robot to navigate towards a light source. The vehicle platform is based on the Tamiya TXT-1 chassis which is out\ufb01tted with an onboard computer for processing high-level functions, a microcontroller for controlling the low-level tasks, and an array of sensors for collecting information about its surroundings. The TXT-1 robot is a unique research testbed that encourages the use of a modular design, low-cost COTS hardware, and open-source software. The TXT-1 is designed using different modules or blocks that are separated based on functionality. The different functional blocks of the TXT-1 are the motors, power, low-level controller, high-level controller, and sensors. This modular design is important when considering upgrading or maintaining the robot. The research platform uses an Apple Mac Mini as its on-board computer for handling high-level navigation tasks like processing sensor data and computing navigation trajectories. ROS, the robot operating system, is used on the computer as a development environment to easily implement algorithms to validate on the robot. A ROS driver was created so that the TXT-1 low-level functions can be sensed and commanded. The TXT-1 low-level controller is designed using an ARM7 processor development board with FreeRTOS, OpenOCD, and the CodeSourcery development tools. The RTOS is used to provide a stable, real-time platform that can be used for many future generations of TXT-1 robots. A communication protocol is created so that the high and low-level processors can communicate. A power distribution system is designed and built to deliver power to all of the systems efficiently and reliably while using a single battery type. Velocity controllers are developed and implemented on the low-level controller. These control the linear and angular velocities using the wheel encoders in a PID feedback loop. The angular velocity controller uses gain scheduling to overcome the systems nonlinearity. The controllers are then tested for adequate velocity response and tracking. The robot is then tested by using the Q-Learning algorithm to teach the robot to navigate towards a light source. The Q-Learning algorithm is \ufb01rst described in detail, and then the problem is formulated and the algorithm is tested in the Stage simulation environment with ROS. The same ROS code is then used on the TXT-1 to implement the algorithm in hardware. Because of delays encountered in the system, the Q-Learning algorithm is modi\ufb01ed to use the sensed action to update the Q-Table, which gives promising results. As a result of this research, a novel autonomous ground vehicle was built and the Q-Learning source \ufb01nding problem was implemented.' Advisors/Committee Members: Fierro, Rafael, Oishi, Meeko, Lumia, Ron.

Subjects/Keywords: Autonomous robots – Design and construction; Reinforcement learning; Feedback control systems; Intelligent transportation systems – Design and construction.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Appel, T. (2012). The development of a robotic test bed with applications in Q-learning. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/17331

Chicago Manual of Style (16^th Edition):

Appel, Titus. “The development of a robotic test bed with applications in Q-learning.” 2012. Masters Thesis, University of New Mexico. Accessed April 15, 2021. http://hdl.handle.net/1928/17331.

MLA Handbook (7^th Edition):

Appel, Titus. “The development of a robotic test bed with applications in Q-learning.” 2012. Web. 15 Apr 2021.

Vancouver:

Appel T. The development of a robotic test bed with applications in Q-learning. [Internet] [Masters thesis]. University of New Mexico; 2012. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/1928/17331.

Council of Science Editors:

Appel T. The development of a robotic test bed with applications in Q-learning. [Masters Thesis]. University of New Mexico; 2012. Available from: http://hdl.handle.net/1928/17331

23. Khodayi-mehr, Reza. MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS .

Degree: 2019, Duke University

URL: http://hdl.handle.net/10161/18683

► Mathematical models that describe different processes and phenomena are of paramount importance in many robotics applications. Nevertheless, utilization of high-fidelity models, particularly Partial Differential… (more)

▼ Mathematical models that describe different processes and phenomena are of paramount importance in many robotics applications. Nevertheless, utilization of high-fidelity models, particularly Partial Differential Equations (PDEs), has been hindered for many years due to the lack of adequate computational resources onboard mobile robots. One such problem of interest for the roboticists, that can hugely benefit from more descriptive models, is Chemical Plume Tracing (CPT). In the CPT problem, one or multiple mobile robots are equipped with chemical concentration and flow sensors and attempt to localize chemical sources in an environment of interest. This problem has important applications ranging from environmental monitoring and protection to search and rescue missions. The transport of a chemical in a fluid medium is mathematically modeled by the Advection-Diffusion (AD) Partial Differential Equation (PDE). Despite versatility, rigorous derivation, and powerful descriptive nature, the AD-PDE has seldom been used in its general form for the solution of the CPT problem due to high computational cost. Instead, often simplified scenarios that render closed-form solutions for the AD-PDE or various heuristics are used in the robotics literature. Using the AD-PDE to model the transport phenomenon enables generalization of the CPT problem to estimate other properties of the sources, e.g., their intensity, in addition to their locations. We refer to this problem as Source Identification (SI) which we define as the problem of estimating the properties of the sources using concentration measurements that are generated under the action of those sources. We can also put one step further and consider the problem of controlling a set of sources, carried by a team of mobile robots, to generate and maintain desired concentration levels in select regions of the environment with the objective of cloaking those regions from external environmental conditions; we refer to this problem as the AD-PDE control problem that has important applications in search and rescue missions. Both SI and AD-PDE control problems can be formulated as PDE-constrained optimization problems. Solving such optimization problems onboard mobile robots is challenging due to the following reasons: (i) the computational cost of solving the AD-PDE using traditional numerical discretization schemes, e.g., the Finite Element (FE) method, is prohibitively high, (ii) obtaining accurate knowledge of the environment and Boundary and Initial Conditions (BICs), required to solve the AD-PDE, is difficult and prone to error and finally, (iii) obtaining accurate estimates of the velocity and diffusivity fields is challenging since for typical transport mediums like air even in very small velocities, the flow is turbulent. In addition, we need to plan the actions of the mobile robots, e.g., measurement collection for SI or release rates for the AD-PDE control problem, to ensure that they accomplish their tasks optimally. This can be done by formulating a planning… Advisors/Committee Members: Zavlanos, Michael M (advisor).

Subjects/Keywords: Robotics; Computer science; Statistics; Active Sensing; Advection-Diffusion PDE; Learning and Control; Mobile Robots; Neural Networks; Source Identification

10 more images…

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APA (6^th Edition):

Khodayi-mehr, R. (2019). MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/18683

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Khodayi-mehr, Reza. “MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS .” 2019. Thesis, Duke University. Accessed April 15, 2021. http://hdl.handle.net/10161/18683.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Khodayi-mehr, Reza. “MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS .” 2019. Web. 15 Apr 2021.

Vancouver:

Khodayi-mehr R. MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS . [Internet] [Thesis]. Duke University; 2019. [cited 2021 Apr 15]. Available from: http://hdl.handle.net/10161/18683.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khodayi-mehr R. MODEL-BASED LEARNING AND CONTROL OF ADVECTION-DIFFUSION TRANSPORT USING MOBILE ROBOTS . [Thesis]. Duke University; 2019. Available from: http://hdl.handle.net/10161/18683

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

24. Na Yu. Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>.

Degree: Aerospace and Mechanical Engineering, 2016, University of Notre Dame

URL: https://curate.nd.edu/show/p8418k7434s

► Current research has shown that building energy consumption contributes to more than 40% of the total energy use in United States. In recent years,… (more)

▼ Current research has shown that building energy consumption contributes to more than 40% of the total energy use in United States. In recent years, the investigation of the reduction of a building’s energy consumption, both at the design stage and operational stage, has received great attention. To obtain an optimal design and improve energy efficiency during operation, a reliable building energy model is needed to incorporate the important static and dynamic information from various resources. Static information includes building geometry, materials, and installed HVAC system, while dynamic information includes weather information, occupants’ temperature schedule, inputs from sensors (e.g., temperature and occupancy), and internal heat and moisture sources during operation. At the design stage, using a building energy model can assist architects with normative design and building optimization workflows. The developed energy model in this dissertation can be easily integrated with Revit Architecture, one of the most commonly used building design tools, to accelerate the design optimization process. Its use can save redundant efforts of going back-and-forth between an energy modeling tool and a design tool to assess the energy impact of design modifications. At the operational stage, a machine learning method is utilized to predict the occupants temperature schedule, and this schedule is input to the model-based predictive control for further optimization with the goal of minimizing energy consumption while maintaining comfort conditions. Though many attempts have been made to study the basic mechanisms of building energy modeling and predictive control, major challenge still lie in how to develop a simple but also relatively accurate thermal model to simulate a building’s energy performance. The other challenge lie in how to develop a comprehensive cost function with sufficient constraints for a model predictive control algorithm. This dissertation introduces and illustrates a method for integrated building control to reduce energy consumption and maintain indoor temperature setpoints, based on the prediction of occupant behavior patterns and weather information. It primarily focuses on the following goals. Firstly, An energy model for building energy simulation has been developed, and integrated to assist architects to do design optimizations. Secondly, a Model-Based Predictive Control (MBPC) method has been developed and experiments have been conducted to validate the energy-saving benefits of MBPC compared with other conventional control methods. In the end, a machine-learning algorithm is developed to predict the future temperature schedule based on historic data. Advisors/Committee Members: Ashley Thrall, Committee Member, Panos Antsaklis, Committee Member, Samuel Paolucci, Research Director, Mihir Sen, Committee Member.

Subjects/Keywords: Building energy modeling; model-based predictive control; decision-tree learning

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APA (6^th Edition):

Yu, N. (2016). Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/p8418k7434s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yu, Na. “Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>.” 2016. Thesis, University of Notre Dame. Accessed April 15, 2021. https://curate.nd.edu/show/p8418k7434s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yu, Na. “Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>.” 2016. Web. 15 Apr 2021.

Vancouver:

Yu N. Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>. [Internet] [Thesis]. University of Notre Dame; 2016. [cited 2021 Apr 15]. Available from: https://curate.nd.edu/show/p8418k7434s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu N. Model-Based Design Optimization and Predictive Control to Minimize Energy Consumption of a Building</h1>. [Thesis]. University of Notre Dame; 2016. Available from: https://curate.nd.edu/show/p8418k7434s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

UCLA

25. Lakshmipathy, Arjun Sriram. Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control.

Degree: Computer Science, 2018, UCLA

URL: http://www.escholarship.org/uc/item/4770b06c

► This thesis presents a novel, biomimetic model of the eye for realistic virtual human anima- tion. We also introduce a deep learning approach to oculomotor… (more)

Subjects/Keywords: Computer science; Biomechanical human animation; Biomimetic Vision; Deep learning; Eye modeling; Oculomotor Control; Sensorimotor Control

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APA (6^th Edition):

Lakshmipathy, A. S. (2018). Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4770b06c

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lakshmipathy, Arjun Sriram. “Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control.” 2018. Thesis, UCLA. Accessed April 15, 2021. http://www.escholarship.org/uc/item/4770b06c.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lakshmipathy, Arjun Sriram. “Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control.” 2018. Web. 15 Apr 2021.

Vancouver:

Lakshmipathy AS. Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control. [Internet] [Thesis]. UCLA; 2018. [cited 2021 Apr 15]. Available from: http://www.escholarship.org/uc/item/4770b06c.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lakshmipathy AS. Biomimetic Modeling of the Eye and Deep Neuromuscular Oculomotor Control. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/4770b06c

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Carnegie Mellon University

26. Silver, David. Learning Preference Models for Autonomous Mobile Robots in Complex Domains.

Degree: 2010, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/551

► Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. As the environments and scenarios to which… (more)

▼ Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parameters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the simplest of circumstances. This thesis presents the development and application of machine learning techniques that automate the construction and tuning of preference models within complex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback. The effectiveness of these approaches is confirmed through application to several real world domains, such as the interpretation of static and dynamic perceptual data in unstructured environments and the learning of human driving styles and maneuver preferences. Extensive testing and experimentation both in simulation and in the field with multiple mobile robotic systems provides empirical confirmation of superior autonomous performance, with less expert interaction and no hand tuning. These experiments validate the potential applicability of the developed algorithms to a large variety of future mobile robotic systems.

Subjects/Keywords: Mobile Robots; Field Robotics; Learning from Demonstration; Imitation Learning; Inverse Optimal Control; Active Learning; Preference Models; Cost Functions; Parameter Tuning; Artificial Intelligence and Robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Silver, D. (2010). Learning Preference Models for Autonomous Mobile Robots in Complex Domains. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Silver, David. “Learning Preference Models for Autonomous Mobile Robots in Complex Domains.” 2010. Thesis, Carnegie Mellon University. Accessed April 15, 2021. http://repository.cmu.edu/dissertations/551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Silver, David. “Learning Preference Models for Autonomous Mobile Robots in Complex Domains.” 2010. Web. 15 Apr 2021.

Vancouver:

Silver D. Learning Preference Models for Autonomous Mobile Robots in Complex Domains. [Internet] [Thesis]. Carnegie Mellon University; 2010. [cited 2021 Apr 15]. Available from: http://repository.cmu.edu/dissertations/551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Silver D. Learning Preference Models for Autonomous Mobile Robots in Complex Domains. [Thesis]. Carnegie Mellon University; 2010. Available from: http://repository.cmu.edu/dissertations/551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

27. Koryakovskiy, I. Safer reinforcement learning for robotics.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; 7923c257-e81f-4e29-adf7-bd6014d9da6a ; 10.4233/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:isbn:978-5-00058-959-5 ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a

► Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of… (more)

Subjects/Keywords: Reinforcement Learning; Humanoid Robots; Optimal Control; Learning and Adaptive Systems; Nonlinear Model Predictive Control; Parametric Uncertainties; Structural Uncertainties; Bipedal Robots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Koryakovskiy, I. (2018). Safer reinforcement learning for robotics. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; 7923c257-e81f-4e29-adf7-bd6014d9da6a ; 10.4233/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:isbn:978-5-00058-959-5 ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a

Chicago Manual of Style (16^th Edition):

Koryakovskiy, I. “Safer reinforcement learning for robotics.” 2018. Doctoral Dissertation, Delft University of Technology. Accessed April 15, 2021. http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; 7923c257-e81f-4e29-adf7-bd6014d9da6a ; 10.4233/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:isbn:978-5-00058-959-5 ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a.

MLA Handbook (7^th Edition):

Koryakovskiy, I. “Safer reinforcement learning for robotics.” 2018. Web. 15 Apr 2021.

Vancouver:

Koryakovskiy I. Safer reinforcement learning for robotics. [Internet] [Doctoral dissertation]. Delft University of Technology; 2018. [cited 2021 Apr 15]. Available from: http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; 7923c257-e81f-4e29-adf7-bd6014d9da6a ; 10.4233/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:isbn:978-5-00058-959-5 ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a.

Council of Science Editors:

Koryakovskiy I. Safer reinforcement learning for robotics. [Doctoral Dissertation]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; 7923c257-e81f-4e29-adf7-bd6014d9da6a ; 10.4233/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; urn:isbn:978-5-00058-959-5 ; urn:NBN:nl:ui:24-uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a ; http://resolver.tudelft.nl/uuid:7923c257-e81f-4e29-adf7-bd6014d9da6a

University of Notre Dame

28. Arash Rahnama. Learning-Based Approaches to Control, Estimation and Modeling</h1>.

Degree: Electrical Engineering, 2018, University of Notre Dame

URL: https://curate.nd.edu/show/bk128913b51

► Design of cyber-physical systems requires control approaches that provide stability, performance, compositionality, robustness and security. QSR-Dissipativite and passive dynamical systems exhibit stability and robustness… (more)

▼ Design of cyber-physical systems requires control approaches that provide stability, performance, compositionality, robustness and security. QSR-Dissipativite and passive dynamical systems exhibit stability and robustness and preserve their properties over feedback and parallel interconnections. The majority of large-scale systems are designed such that their sub-systems communicate with each other over a band-limited communication network. Event-triggered network control designs are capable of decreasing the communication load amongst sub-systems while maintaining the desired performance index and robustness. In the present dissertation, first a comprehensive design-based event-triggered network control approach for the design and control of large-scale cyber-physical systems is introduced and a complete analysis of the passivity and QSR-Dissipativitity properties of the framework is given. Second, this work is expanded to a control methodology that deals with the negative effects of communication networks such as information loss, quantization and time-delays which may degrade the stability, performance and robustness of the overall networked system. The proposed control framework is easy to design and only utilizes the dynamical systems’ sensed input-output data and does not require an exact knowledge of the systems’ mathematical models. Next, a passivity based event-triggered control framework for synchronization of multi-agent systems is introduced and its resilience and robustness against Byzantine attacks are characterized. Further, a detection, estimation and decision making platform for each agent which relies on the statistical distribution of the data points received from neighboring agents is introduced. Lastly, learning-based control methodologies for mitigating the negative effects of the attack on synchronization are presented. The last chapter consists of three application oriented works pertaining to control, estimation and modeling. First, a co-simulation based online learning framework for performance optimization of an adaptive cruise controller which can deal with environment’s uncertainties and time-delays is introduced. In the second application, an unsupervised deep learning architecture for modeling and estimation of the cognitive states in the human brain, independent of subjects, using fMRI signals is introduced. Lastly, a supervised deep learning framework and an ensemble learning architecture for classification of 3D protein structures are proposed and their performance indices are compared and contrasted. Advisors/Committee Members: Hai Lin, Committee Member, Vijay Gupta, Committee Member, Panos J. Antsaklis, Research Director, Peter Bauer, Committee Member.

Subjects/Keywords: Learning-based control; Systems and Control; Multi-Agent Systems; Networked Systems; Machine Learning; Estimation, Modeling and Prediction; Cyber-Physical Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rahnama, A. (2018). Learning-Based Approaches to Control, Estimation and Modeling</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/bk128913b51

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rahnama, Arash. “Learning-Based Approaches to Control, Estimation and Modeling</h1>.” 2018. Thesis, University of Notre Dame. Accessed April 15, 2021. https://curate.nd.edu/show/bk128913b51.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rahnama, Arash. “Learning-Based Approaches to Control, Estimation and Modeling</h1>.” 2018. Web. 15 Apr 2021.

Vancouver:

Rahnama A. Learning-Based Approaches to Control, Estimation and Modeling</h1>. [Internet] [Thesis]. University of Notre Dame; 2018. [cited 2021 Apr 15]. Available from: https://curate.nd.edu/show/bk128913b51.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahnama A. Learning-Based Approaches to Control, Estimation and Modeling</h1>. [Thesis]. University of Notre Dame; 2018. Available from: https://curate.nd.edu/show/bk128913b51

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

UCLA

29. Teng, Kuo-Tai. Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control.

Degree: Mechanical Engineering, 2014, UCLA

URL: http://www.escholarship.org/uc/item/91c52795

► This thesis develops learning control algorithms for power converters and precisionmotion control. The repetitive control is designed for power converters to providezero steady state error… (more)

▼ This thesis develops learning control algorithms for power converters and precisionmotion control. The repetitive control is designed for power converters to providezero steady state error and harmonic compensation. A model-based iterativelearning control is designed for linear motor to track given reference profile withsub-micron RMS error.The objective of the control design for power converters is to compensate har-monic distortions in the AC side to enhance power factor of the power converter.For power inverter, implementations focus on compensating harmonic distortionsin the output AC voltage; For power rectifier, the objective is to compensateharmonic distortions in the input AC current.In order to compensate harmonics for power converters, the prototype repeti-tive control [TTC89] is first being applied to power inverter in fixed frame. How-ever, the power rectifier is not a linear system. To linearize the system at a moremeaningful equilibrium point, a D-Q transformation is applied. But the originalsingle-input single-output system become multi-input multi-output system in D-Qrotating frame, the famous prototype repetitive control design mythology can notbe applied directly.Repetitive control for multi-input multi-output system is developed for thecontrol of power converters in D-Q rotating frame. The coupled dynamics inthe multi-input multi-output system is first decoupled by utilizing the Smith-McMillan decomposition. Then the prototype repetitive control design is appliedto the decoupled single-input single-output system.In the precision motion control, model-based iterative learning control is pro-posed to achieve sub-micron RMS tracking error. The learning fiter in iterativelearning control determines the performance in terms of convergence rate and con-verged error. The ideal learning filter is the inverse of the system being learned.For non-minimum phase system, direct system inversion would result in an un-stable filter.In this thesis, a data-based dynamic inversion method in frequency domainis proposed. Different inversion filter was investigated in the thesis includingZero-Phase-Error-Tracking-Controller (ZPETC), Zero-Magnitude-Error-Tacking-Controller (ZMETC), Direct inversion, data-based phase compensator, and theproposed data-based frequency domain inversion.

Subjects/Keywords: Mechanical engineering; Iterative Learning Control; Noncircular Machining; PWM Inverter; PWM Rectifier; Repetitive Control; Repetitive Control for Multi-input Multi-output system

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Teng, K. (2014). Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/91c52795

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Teng, Kuo-Tai. “Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control.” 2014. Thesis, UCLA. Accessed April 15, 2021. http://www.escholarship.org/uc/item/91c52795.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Teng, Kuo-Tai. “Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control.” 2014. Web. 15 Apr 2021.

Vancouver:

Teng K. Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Apr 15]. Available from: http://www.escholarship.org/uc/item/91c52795.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Teng K. Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/91c52795

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

30. Melnyk, Artem. Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach.

Degree: Docteur es, STIC (sciences et technologies de l'information et de la communication) - Cergy, 2014, Cergy-Pontoise; Donecʹkij nacíonalʹnij uníversitet (Ukraine)

URL: http://www.theses.fr/2014CERG0745

►

Les robots intégrés aux chaînes de production sont généralement isolés des ouvriers et ne prévoient pas d'interaction physique avec les humains. Dans le futur, le… (more)

▼

Les robots intégrés aux chaînes de production sont généralement isolés des ouvriers et ne prévoient pas d'interaction physique avec les humains. Dans le futur, le robot humanoïde deviendra un partenaire pour vivre ou travailler avec les êtres humains. Cette coexistence prévoit l'interaction physique et sociale entre le robot et l'être humain. En robotique humanoïde les futurs progrès dépendront donc des connaissances dans les mécanismes cognitifs présents dans les interactions interpersonnelles afin que les robots interagissent avec les humains physiquement et socialement. Un bon exemple d'interaction interpersonnelle est l'acte de la poignée de la main qui possède un rôle social très important. La particularité de cette interaction est aussi qu'elle est basée sur un couplage physique et social qui induit une synchronisation des mouvements et des efforts. L'intérêt d'étudier la poignée de main pour les robots consiste donc à élargir leurs propriétés comportementales pour qu'ils interagissent avec les humains de manière plus habituelle.Cette thèse présente dans un premier chapitre un état de l'art sur les travaux dans les domaines des sciences humaines, de la médecine et de la robotique humanoïde qui sont liés au phénomène de la poignée de main. Le second chapitre, est consacré à la nature physique du phénomène de poignée de main chez l'être humain par des mesures quantitatives des mouvements. Pour cela un système de mesures a été construit à l'Université Nationale Technique de Donetsk (Ukraine). Il est composé d'un gant instrumenté par un réseau de capteurs portés qui permet l'enregistrement des vitesses et accélérations du poignet et les forces aux points de contact des paumes, lors de l'interaction. Des campagnes de mesures ont permis de montrer la présence d'un phénomène de synchronie mutuelle précédé d'une phase de contact physique qui initie cette synchronie. En tenant compte de cette nature rythmique, un contrôleur à base de neurones rythmiques de Rowat-Selverston, intégrant un mécanisme d'apprentissage de la fréquence d'interaction, est proposé et etudié dans le troisième chapitre pour commander un bras robotique. Le chapitre quatre est consacré aux expériences d'interaction physique homme/robot. Des expériences avec un bras robotique Katana montrent qu'il est possible d'apprendre à synchroniser la rythmicité du robot avec celle imposée par une per-sonne lors d'une poignée de main grâce à ce modèle de contrôleur bio-inspiré. Une conclusion générale dresse le bilan des travaux menés et propose des perspectives.

Automated production lines integrate robots which are isolated from workers, so there is no physical interaction between a human and robot. In the near future, a humanoid robot will become a part of the human environment as a companion to help or work with humans. The aspects of coexistence always presuppose physical and social interaction between a robot and a human. In humanoid robotics, further progress depends on knowledge of cognitive mechanisms of interpersonal interaction as robots physically…

Advisors/Committee Members: Hénaff, Patrick (thesis director), Borysenko, Volodymyr (thesis director).

Subjects/Keywords: Commande bio-Inspirée; Reseaux des neurones; Interaction physique; Robotique humanoide; Automatique; Apprentissage; Bio-Inspired control; Neural networks; Physical interaction; Humanoid robots; Control; Learning

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Melnyk, A. (2014). Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach. (Doctoral Dissertation). Cergy-Pontoise; Donecʹkij nacíonalʹnij uníversitet (Ukraine). Retrieved from http://www.theses.fr/2014CERG0745

Chicago Manual of Style (16^th Edition):

Melnyk, Artem. “Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach.” 2014. Doctoral Dissertation, Cergy-Pontoise; Donecʹkij nacíonalʹnij uníversitet (Ukraine). Accessed April 15, 2021. http://www.theses.fr/2014CERG0745.

MLA Handbook (7^th Edition):

Melnyk, Artem. “Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach.” 2014. Web. 15 Apr 2021.

Vancouver:

Melnyk A. Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach. [Internet] [Doctoral dissertation]. Cergy-Pontoise; Donecʹkij nacíonalʹnij uníversitet (Ukraine); 2014. [cited 2021 Apr 15]. Available from: http://www.theses.fr/2014CERG0745.

Council of Science Editors:

Melnyk A. Perfectionnement des algorithmes de contrôle-commande des robots manipulateur électriques en interaction physique avec leur environnement par une approche bio-inspirée : Improvement of control algorithms of electrical robot arms in physical interaction with their environment with bio-inspired approach. [Doctoral Dissertation]. Cergy-Pontoise; Donecʹkij nacíonalʹnij uníversitet (Ukraine); 2014. Available from: http://www.theses.fr/2014CERG0745

Open Access Theses and Dissertations