subject:(robot learning)

Georgia Tech

1. Bullard, Kalesha. Managing learning interactions for collaborative robot learning.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

► Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge… (more)

▼ Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge is not all human partners will be proficient at teaching; furthermore, humans should not be held accountable for tracking a robot’s knowledge over time in a dynamically changing environment, across multiple tasks. Thus, it is important to enable these interactive robots to characterize their own uncertainty and equip them with an information gathering policy for asking the appropriate questions of their human partners to resolve that uncertainty. In this way, the robot shares the responsibility in guiding its own learning process and is a collaborator in the learning. Additionally, given the robot requires some tutelage from its partner, awareness of constraints on the teacher’s time and cognitive resources available for devoting to the interaction could help the agent to use the time allotted more wisely. This thesis examines the problem of enabling a robotic agent to leverage structured interaction with a human partner for acquiring concepts relevant to a task it must later perform. To equip the agent with the desired concept knowledge, we first explore the paradigm of Learning from Demonstration for the acquisition of (1) training instances as examples of task-relevant concepts and (2) informative features for appropriately representing and discriminating between task-relevant concepts. Given empirical evidence that a human partner can be helpful to the agent in solving the concept learning problem, we subsequently investigate the design of algorithms that enable the robot learner to autonomously manage interaction with its human partner, using a questioning policy to actively gather both instance and feature information. This thesis seeks to investigate the following hypothesis: In the context of robot learning from human demonstrations in changeable and resource-constrained environments, enabling the robot to actively elicit multiple types of information through questions, and to reason about what question to ask and when, leads to improved learning performance. Advisors/Committee Members: Chernova, Sonia (advisor), Isbell, Charles (committee member), Christensen, Henrik I. (committee member), Mataric, Maja (committee member), Thomaz, Andrea L. (committee member).

Subjects/Keywords: Interactive robot learning; Active learning

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APA (6^th Edition):

Bullard, K. (2019). Managing learning interactions for collaborative robot learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62294

Chicago Manual of Style (16^th Edition):

Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 11, 2021. http://hdl.handle.net/1853/62294.

MLA Handbook (7^th Edition):

Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Web. 11 Apr 2021.

Vancouver:

Bullard K. Managing learning interactions for collaborative robot learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1853/62294.

Council of Science Editors:

Bullard K. Managing learning interactions for collaborative robot learning. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62294

Georgia Tech

2. Rana, Muhammad Asif. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.

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

URL: http://hdl.handle.net/1853/64106

► Functioning in the real world requires robots to reason about and generate motions for execution of complex tasks, in potentially unstructured and dynamic environments. Early… (more)

▼ Functioning in the real world requires robots to reason about and generate motions for execution of complex tasks, in potentially unstructured and dynamic environments. Early generations of robots were limited to simple tasks in controlled environments, where only a single skill was often required. To deal with the diversity of tasks and environments associated with the real world, robots should instead have access to a library of skills. Instead of pre-programming all the desired skills, a procedure which is cumbersome and often infeasible, it is beneficial to have a framework that allows robots to acquire new skills when required. One such framework is learning from demonstration, which provides a channel for robots to learn skills from everyday users. This dissertation provides methods for learning skills from human demonstrations. Skill learning from human demonstrations carries certain challenges. Skills can be vastly different, enforcing a range of motion constraints. Human demonstrations are also often limited in number. Lastly, generalization of learned skills can be tied to generating motions that need to satisfy additional pre-specified constraints. These constraints can be associated with feasibility, requiring motions compliant with robot's kinematics and its environment, or they may be linked to coordination, requiring correlated motions of several robot body parts. To contend with the diversity of skills, the presence of feasibility and coordination constraints, and the scarcity of data, it is beneficial to impose structure in the skill representation. The structure incorporates domain knowledge in the representation, enabling desirable generalization even when access to large amounts data is hard. The objective of this dissertation is to develop a family of techniques that allow robots to sample-efficiently learn diverse skills from human demonstrations, and subsequently generalize the skills to novel contexts while satisfying additional constraints that may exist, concerning the feasibility and coordination of robot motions. Each proposed method comes with a structured representation, suitable for tackling the challenges associated with a subset of skills. Specifically, we present: (i) a structured multi-coordinate cost learning framework coupled with an optimization routine, that generalizes skills requiring preservation of multiple geometric properties of motions, (ii) a structured prior representation employed in a probabilistic inference framework, geared towards generating optimal and feasibility-constrained motions, (iii) a stable dynamical system representation, suitable for learning skills aimed at motions that can react instantly to dynamic perturbation, and (iv) a tree-structured stable dynamical system which synthesizes multiple dynamical system into one, and learns skills dictating feasible and coordinated, yet reactive robot motions. As a preliminary to the aforementioned learning techniques, this dissertation also provides an over-arching benchmarking effort to identify the key… Advisors/Committee Members: Chernova, Sonia (advisor), Boots, Byron (committee member), Hutchinson, Seth (committee member), Gombolay, Matthew (committee member), Hermans, Tucker (committee member).

Subjects/Keywords: learning from demonstration; robot learning

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

APA (6^th Edition):

Rana, M. A. (2020). Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64106

Chicago Manual of Style (16^th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 11, 2021. http://hdl.handle.net/1853/64106.

MLA Handbook (7^th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Web. 11 Apr 2021.

Vancouver:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1853/64106.

Council of Science Editors:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64106

University of Texas – Austin

3. Shah, Rishi Alpesh. Deep R learning for continual area sweeping.

Degree: MSin Computational Science, Engineering, and Mathematics, Computational Science, Engineering, and Mathematics, 2019, University of Texas – Austin

URL: http://dx.doi.org/10.26153/tsw/8201

► In order to maintain robustness, autonomous robots need to constantly update their knowledge of the environment, which can be expensive when they are deployed in… (more)

Subjects/Keywords: Machine learning; Reinforcement learning; Robotics; Robot learning

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APA (6^th Edition):

Shah, R. A. (2019). Deep R learning for continual area sweeping. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/8201

Chicago Manual of Style (16^th Edition):

Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Masters Thesis, University of Texas – Austin. Accessed April 11, 2021. http://dx.doi.org/10.26153/tsw/8201.

MLA Handbook (7^th Edition):

Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Web. 11 Apr 2021.

Vancouver:

Shah RA. Deep R learning for continual area sweeping. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2021 Apr 11]. Available from: http://dx.doi.org/10.26153/tsw/8201.

Council of Science Editors:

Shah RA. Deep R learning for continual area sweeping. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/8201

Delft University of Technology

4. Capelle, Lotte (author). Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9a6117f6-91d2-4b4d-92e6-a66de49c554b

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This thesis describes three different predictive frameworks which are applied to improve the interaction behavior between a robot and their users. Machine learning is used… (more)

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This thesis describes three different predictive frameworks which are applied to improve the interaction behavior between a robot and their users. Machine learning is used to train the frameworks. The training data contains information obtained from one user over a period of approximately 3 to 30 days. This work is applied to the LEA robot: an elderly assistant robot. Each framework is implemented in an exciting real problem with the aim to improve life quality of elderly. The first framework predicts when and from where the user calls the robot to go towards the user on a daily basis. The repetitive events are predicted with an accuracy of 98%. The framework starts to give correct predictions from four examples only. This framework uses a density-based clustering algorithm in combination with a shifting window over the input data. This combination creates a predictive framework that adapts to the user behavior changes. The second framework is used to filter undesired warning messages such as e.g. obstacle warnings while walking. The true positive rate is measured to be 100% for events that occur at least once in the three days. The algorithm creates a grid map which is used to memorize trajectories that lead to undesired warning messages. The last framework predicts the user destination while walking. This is an important aid for patients suffering from dementia. The framework uses the trajectory and the starting time and location as input. A forward neural network is used to classify the room destination of the user. The network has two input layers. The first input layer uses a part of the trajectory and the second input layer is an embedding layer which reduces the size of "metadata". The framework classifies 66.6 % of the time with a certainty above 85%. When only considering predictions with a classifications certainty above 85%, a predictive accuracy of 97.3% is measured. If all classifications are considered, the framework showed to be 86.4 % accurate, whereas an alternative approach like support vector machines showed to have a predictive accuracy of 60 % on this problem.

Systems and Control

Advisors/Committee Members: Mazo Espinosa, M. (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Machine Learning; Small data; Robot

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APA (6^th Edition):

Capelle, L. (. (2018). Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9a6117f6-91d2-4b4d-92e6-a66de49c554b

Chicago Manual of Style (16^th Edition):

Capelle, Lotte (author). “Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot.” 2018. Masters Thesis, Delft University of Technology. Accessed April 11, 2021. http://resolver.tudelft.nl/uuid:9a6117f6-91d2-4b4d-92e6-a66de49c554b.

MLA Handbook (7^th Edition):

Capelle, Lotte (author). “Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot.” 2018. Web. 11 Apr 2021.

Vancouver:

Capelle L(. Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Apr 11]. Available from: http://resolver.tudelft.nl/uuid:9a6117f6-91d2-4b4d-92e6-a66de49c554b.

Council of Science Editors:

Capelle L(. Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9a6117f6-91d2-4b4d-92e6-a66de49c554b

NSYSU

5. Chung, Chi-Hsiu. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.

Degree: Master, Information Management, 2009, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

► This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With… (more)

Subjects/Keywords: Genetic Algorithm; Imitation Learning; Robot Learning

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APA (6^th Edition):

Chung, C. (2009). Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

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):

Chung, Chi-Hsiu. “Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.” 2009. Thesis, NSYSU. Accessed April 11, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840.

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

MLA Handbook (7^th Edition):

Chung, Chi-Hsiu. “Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.” 2009. Web. 11 Apr 2021.

Vancouver:

Chung C. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Apr 11]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840.

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

Council of Science Editors:

Chung C. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

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

Delft University of Technology

6. Jauhri, Snehal (author). Interactive Learning in State-space: Enabling robots to learn from non-expert humans.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

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Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require… (more)

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Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require demonstration data (in the form of state-action labels) and in many scenarios, the demonstrations can be expensive to obtain or too complex for a demonstrator to execute. This lack or sub-optimality of demonstrations limits the applicability and performance of many Imitation Learning methods. Advancements in Interactive Imitation Learning techniques however, have made it easier for demonstrators to train agents and improve their performance. These techniques involve demonstrators interacting with and guiding the agent as it performs the requisite task. This guidance is typically in the form of corrections or feedback on the current actions being executed by the agent. In this thesis, a novel Interactive Learning technique is proposed that uses human corrective feedback in state-space to train and improve agent behavior. This technique is beneficial since providing guidance to the agent in terms of `changing its state' is often easier or more intuitive for the human demonstrator (as opposed to changing the actions being executed). For instance, in manipulation tasks using a robotic arm, it is easier for the demonstrator to provide state information such as the Cartesian position of the end-effector rather than low-level action information such as joint angles. Keeping such scenarios in mind, we propose our method titled: Teaching Imitative Policies in State-space (TIPS). We evaluate the performance of TIPS for various control tasks as part of the OpenAI Gym toolkit as well as for a manipulation task using a KUKA LBR iiwa robotic arm. We show that through continuous improvement via feedback, agents trained using TIPS outperform the demonstrator and in-turn outperform conventional Imitation Learning agents.

Electrical Engineer | Embedded Systems

Advisors/Committee Members: Kober, Jens (mentor), Celemin, Carlos (mentor), van Genderen, Arjan (graduation committee), Peternel, Luka (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Imitation Learning; Learning from Demonstrations; Robot Control

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

APA (6^th Edition):

Jauhri, S. (. (2020). Interactive Learning in State-space: Enabling robots to learn from non-expert humans. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

Chicago Manual of Style (16^th Edition):

Jauhri, Snehal (author). “Interactive Learning in State-space: Enabling robots to learn from non-expert humans.” 2020. Masters Thesis, Delft University of Technology. Accessed April 11, 2021. http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7.

MLA Handbook (7^th Edition):

Jauhri, Snehal (author). “Interactive Learning in State-space: Enabling robots to learn from non-expert humans.” 2020. Web. 11 Apr 2021.

Vancouver:

Jauhri S(. Interactive Learning in State-space: Enabling robots to learn from non-expert humans. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 11]. Available from: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7.

Council of Science Editors:

Jauhri S(. Interactive Learning in State-space: Enabling robots to learn from non-expert humans. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

University of Toronto

7. Moro, Christina. Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction.

Degree: 2018, University of Toronto

URL: http://hdl.handle.net/1807/82909

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Caregivers play a crucial role in assisting seniors having difficulty accomplishing activities of daily living (ADLs) due to physical or cognitive limitations. A global decline… (more)

Subjects/Keywords: human-robot interaction; robot behavior learning; socially assistive robots; 0771

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

APA (6^th Edition):

Moro, C. (2018). Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/82909

Chicago Manual of Style (16^th Edition):

Moro, Christina. “Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction.” 2018. Masters Thesis, University of Toronto. Accessed April 11, 2021. http://hdl.handle.net/1807/82909.

MLA Handbook (7^th Edition):

Moro, Christina. “Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction.” 2018. Web. 11 Apr 2021.

Vancouver:

Moro C. Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction. [Internet] [Masters thesis]. University of Toronto; 2018. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1807/82909.

Council of Science Editors:

Moro C. Learning Socially Assistive Robot Behaviors for Personalized Human-Robot Interaction. [Masters Thesis]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/82909

University of Waterloo

8. Sun de la Cruz, Joseph. Learning Inverse Dynamics for Robot Manipulator Control.

Degree: 2011, University of Waterloo

URL: http://hdl.handle.net/10012/6322

► Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such… (more)

▼ Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR…

Subjects/Keywords: Robot Manipulators; Learning Control; Motion Control; Robot Dynamics

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APA (6^th Edition):

Sun de la Cruz, J. (2011). Learning Inverse Dynamics for Robot Manipulator Control. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6322

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):

Sun de la Cruz, Joseph. “Learning Inverse Dynamics for Robot Manipulator Control.” 2011. Thesis, University of Waterloo. Accessed April 11, 2021. http://hdl.handle.net/10012/6322.

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

MLA Handbook (7^th Edition):

Sun de la Cruz, Joseph. “Learning Inverse Dynamics for Robot Manipulator Control.” 2011. Web. 11 Apr 2021.

Vancouver:

Sun de la Cruz J. Learning Inverse Dynamics for Robot Manipulator Control. [Internet] [Thesis]. University of Waterloo; 2011. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10012/6322.

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

Council of Science Editors:

Sun de la Cruz J. Learning Inverse Dynamics for Robot Manipulator Control. [Thesis]. University of Waterloo; 2011. Available from: http://hdl.handle.net/10012/6322

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

Massey University

9. Wang, Wenhan. Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand .

Degree: 2014, Massey University

URL: http://hdl.handle.net/10179/5865

► This research explores a new approach for building a complex intelligent robot multi-behaviour comprising of a variety of intelligent subsystems that are fused together into… (more)

Subjects/Keywords: Robot control; Robot soccer; Genetic network programming; Fuzzy logic; Reinforcement learning

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APA (6^th Edition):

Wang, W. (2014). Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand . (Thesis). Massey University. Retrieved from http://hdl.handle.net/10179/5865

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):

Wang, Wenhan. “Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand .” 2014. Thesis, Massey University. Accessed April 11, 2021. http://hdl.handle.net/10179/5865.

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

MLA Handbook (7^th Edition):

Wang, Wenhan. “Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand .” 2014. Web. 11 Apr 2021.

Vancouver:

Wang W. Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand . [Internet] [Thesis]. Massey University; 2014. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10179/5865.

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

Council of Science Editors:

Wang W. Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand . [Thesis]. Massey University; 2014. Available from: http://hdl.handle.net/10179/5865

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

University of Waterloo

10. Lin, Daiwei. Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/15648

► This thesis is part of the research activities of the Living Architecture System Group (LASG). LASG develops immersive, interactive art sculptures combining concepts of architecture,… (more)

Subjects/Keywords: reinforcement learning; human-robot interaction; architecture-scale

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APA (6^th Edition):

Lin, D. (2020). Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15648

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):

Lin, Daiwei. “Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning.” 2020. Thesis, University of Waterloo. Accessed April 11, 2021. http://hdl.handle.net/10012/15648.

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

MLA Handbook (7^th Edition):

Lin, Daiwei. “Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning.” 2020. Web. 11 Apr 2021.

Vancouver:

Lin D. Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10012/15648.

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

Council of Science Editors:

Lin D. Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15648

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

Colorado State University

11. Blitch, John G. Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees.

Degree: PhD, Psychology, 2014, Colorado State University

URL: http://hdl.handle.net/10217/82506

► Automation has been known to provide both costs and benefits to experienced humans engaged in a wide variety of operational endeavors. Its influence on skill… (more)

Subjects/Keywords: automation; cognition; learning; neuroscience; robot; training

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

APA (6^th Edition):

Blitch, J. G. (2014). Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/82506

Chicago Manual of Style (16^th Edition):

Blitch, John G. “Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees.” 2014. Doctoral Dissertation, Colorado State University. Accessed April 11, 2021. http://hdl.handle.net/10217/82506.

MLA Handbook (7^th Edition):

Blitch, John G. “Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees.” 2014. Web. 11 Apr 2021.

Vancouver:

Blitch JG. Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees. [Internet] [Doctoral dissertation]. Colorado State University; 2014. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10217/82506.

Council of Science Editors:

Blitch JG. Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees. [Doctoral Dissertation]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82506

University of Miami

12. Nath, Piyali. Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots.

Degree: MS, Computer Science (Arts and Sciences), 2014, University of Miami

URL: https://scholarlyrepository.miami.edu/oa_theses/471

► There are several areas of research in the field of motion learning for robots. RoboCanes, a research group belonging to the Department of Computer Science… (more)

Subjects/Keywords: Parallel optimization; humanoid robot; motion learning

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APA (6^th Edition):

Nath, P. (2014). Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots. (Thesis). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_theses/471

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):

Nath, Piyali. “Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots.” 2014. Thesis, University of Miami. Accessed April 11, 2021. https://scholarlyrepository.miami.edu/oa_theses/471.

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

MLA Handbook (7^th Edition):

Nath, Piyali. “Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots.” 2014. Web. 11 Apr 2021.

Vancouver:

Nath P. Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots. [Internet] [Thesis]. University of Miami; 2014. [cited 2021 Apr 11]. Available from: https://scholarlyrepository.miami.edu/oa_theses/471.

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

Council of Science Editors:

Nath P. Distributed and Parallel Optimization of Captured Human Motions for the Generation of Robust and Stable Motions on Simulated Humanoid Robots. [Thesis]. University of Miami; 2014. Available from: https://scholarlyrepository.miami.edu/oa_theses/471

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

Universitat Pompeu Fabra

13. Vouloutsi, Vasiliki. Learning from a robot: creating synthetic psychologically plausible agents.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2017, Universitat Pompeu Fabra

URL: http://hdl.handle.net/10803/456321

► A causa dels avenços tecnològics, els robots aviat formaran part de la nostra vida diària i interactuaran amb nosaltres de forma freqüent. Que els robots… (more)

Subjects/Keywords: Robot; Synthetic psychologically plausible agents; Learning; 62

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APA (6^th Edition):

Vouloutsi, V. (2017). Learning from a robot: creating synthetic psychologically plausible agents. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/456321

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):

Vouloutsi, Vasiliki. “Learning from a robot: creating synthetic psychologically plausible agents.” 2017. Thesis, Universitat Pompeu Fabra. Accessed April 11, 2021. http://hdl.handle.net/10803/456321.

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

MLA Handbook (7^th Edition):

Vouloutsi, Vasiliki. “Learning from a robot: creating synthetic psychologically plausible agents.” 2017. Web. 11 Apr 2021.

Vancouver:

Vouloutsi V. Learning from a robot: creating synthetic psychologically plausible agents. [Internet] [Thesis]. Universitat Pompeu Fabra; 2017. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10803/456321.

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

Council of Science Editors:

Vouloutsi V. Learning from a robot: creating synthetic psychologically plausible agents. [Thesis]. Universitat Pompeu Fabra; 2017. Available from: http://hdl.handle.net/10803/456321

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

14. Román, Abel Bermúdez. Building and programming an autonomous robot using a Raspberry Pi as a PLC.

Degree: Engineering Science, 2016, University of Skövde

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12786

► PLC programming students are often limited to simulated systems or soft PLCs, because the high price of the hardware and the software licenses make… (more)

Subjects/Keywords: PLC; Raspberry Pi; Robot; Control; learning tools.

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APA (6^th Edition):

Román, A. B. (2016). Building and programming an autonomous robot using a Raspberry Pi as a PLC. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12786

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):

Román, Abel Bermúdez. “Building and programming an autonomous robot using a Raspberry Pi as a PLC.” 2016. Thesis, University of Skövde. Accessed April 11, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12786.

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

MLA Handbook (7^th Edition):

Román, Abel Bermúdez. “Building and programming an autonomous robot using a Raspberry Pi as a PLC.” 2016. Web. 11 Apr 2021.

Vancouver:

Román AB. Building and programming an autonomous robot using a Raspberry Pi as a PLC. [Internet] [Thesis]. University of Skövde; 2016. [cited 2021 Apr 11]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12786.

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

Council of Science Editors:

Román AB. Building and programming an autonomous robot using a Raspberry Pi as a PLC. [Thesis]. University of Skövde; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12786

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

Rice University

15. Losey, Dylan P. Responding to Physical Human-Robot Interaction: Theory and Approximations.

Degree: PhD, Engineering, 2018, Rice University

URL: http://hdl.handle.net/1911/105912

► This thesis explores how robots should respond to physical human interactions. From surgical devices to assistive arms, robots are becoming an important aspect of our… (more)

▼ This thesis explores how robots should respond to physical human interactions. From surgical devices to assistive arms, robots are becoming an important aspect of our everyday lives. Unlike earlier robots – which were developed for carefully regulated factory settings – today's robots must work alongside human end-users, and even facilitate physical interactions between the robot and the human. Within the current state-of-the-art, the human's intentionally applied forces are treated as unwanted disturbances that the robot should avoid, reject, or ignore: once the human stops interacting, these robots simply return to their original behavior. By contrast, we recognize that physical interactions are really an implicit form of communication: the human is applying forces and torques to correct the robot's behavior, and teach the robot how it should complete its task. Within this work, we demonstrate that optimally responding to physical human interactions results in robots that learn from these corrections and change their underlying behavior. We first formalize physical human-robot interaction as a partially observable dynamical system, where the human's applied forces and torques are observations about the objective function that the robot should be optimizing, and, more specifically, the human's preferences for how the robot should behave. Solving this system defines the right way for a robot to respond to physical corrections. We derive three approximate solutions for real-time implementation on robotic hardware: these different approximations assume increasing amounts of structure, and consider cases where the robot is given (a) an arbitrary initial trajectory, (b) a parameterized initial trajectory, or (c) the task-related features. We next extend our approximations to account for noisy and imperfect end-users, who may accidentally correct the robot more or less than they intended. We enable robots to reason over what aspects of the human's interaction were intentional, and which of the human's preferences are still unclear. Our overall approach to physical human-robot interaction provides a theoretical basis for robots that both realize why the human is interacting and personalize their behavior in response to that end-user. The feasibility of our theoretical contributions is demonstrated through simulations and user studies. Advisors/Committee Members: O'Malley, Marcia K (advisor).

Subjects/Keywords: human-robot interaction; machine learning; optimal control

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

APA (6^th Edition):

Losey, D. P. (2018). Responding to Physical Human-Robot Interaction: Theory and Approximations. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105912

Chicago Manual of Style (16^th Edition):

Losey, Dylan P. “Responding to Physical Human-Robot Interaction: Theory and Approximations.” 2018. Doctoral Dissertation, Rice University. Accessed April 11, 2021. http://hdl.handle.net/1911/105912.

MLA Handbook (7^th Edition):

Losey, Dylan P. “Responding to Physical Human-Robot Interaction: Theory and Approximations.” 2018. Web. 11 Apr 2021.

Vancouver:

Losey DP. Responding to Physical Human-Robot Interaction: Theory and Approximations. [Internet] [Doctoral dissertation]. Rice University; 2018. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1911/105912.

Council of Science Editors:

Losey DP. Responding to Physical Human-Robot Interaction: Theory and Approximations. [Doctoral Dissertation]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105912

Delft University of Technology

16. de Jong, Tobias (author). The effect of sampling methods on Deep Q-Networks in robot navigation tasks.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98

►

Enabling mobile robots to autonomously navigate complex environments is essential for real-world deployment in commercial, industrial, military, health care, and domestic settings. Prior methods approach… (more)

Subjects/Keywords: Deep Learning; Experience Replay; Robot Navigation; Sampling

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

APA (6^th Edition):

de Jong, T. (. (2019). The effect of sampling methods on Deep Q-Networks in robot navigation tasks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98

Chicago Manual of Style (16^th Edition):

de Jong, Tobias (author). “The effect of sampling methods on Deep Q-Networks in robot navigation tasks.” 2019. Masters Thesis, Delft University of Technology. Accessed April 11, 2021. http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98.

MLA Handbook (7^th Edition):

de Jong, Tobias (author). “The effect of sampling methods on Deep Q-Networks in robot navigation tasks.” 2019. Web. 11 Apr 2021.

Vancouver:

de Jong T(. The effect of sampling methods on Deep Q-Networks in robot navigation tasks. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 11]. Available from: http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98.

Council of Science Editors:

de Jong T(. The effect of sampling methods on Deep Q-Networks in robot navigation tasks. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98

Delft University of Technology

17. Moerdijk, M.M. (author). Learning to walk using minimum prior knowledge: And a small hexapod robot.

Degree: 2013, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:fafd5dd4-653c-4b39-965f-c48707a2c1ca

►

Future robotic systems are expected to be so complex, that the required information – or prior knowledge – to program them is not always available… (more)

▼

Future robotic systems are expected to be so complex, that the required information – or prior knowledge – to program them is not always available and has thus to be discovered in an alternative way. This can be done using Reinforcement Learning (RL) algorithms, that learn from experience. This way of programming can also be beneficial for current robotic systems. For example robots where small changes in the hardware due to: design evolutions, repairs or production tolerances require updates to the control algorithms. A algorithm that learns to complete a task without prior knowledge could do this regardless of the changes and thus make frequent, time consuming, reprogramming and/or tuning unnecessary. The robot used in this thesis is a small ant like hexapod robot that is still in development. It is developed to take advantage of the high volume to power ratio, at low weights of Shape Memory Alloy actuators and to show collaborative behaviour. The task is to find a controller that lets this robot walk as fast as possible from A to B while using minimum prior knowledge. Three approaches to achieve this are proposed in this thesis, these are in descending amount of required prior knowledge: Min-Max, Cost Function Search and the Flat approach. All use a hierarchical division, where low level actions – like forward or left – and the combination of these actions, to reach the goal are learned separately. The approaches differ in the way they learn the low level actions. Min-Max uses fully defined sub-goals that are assumed to be sufficient to solve the problem. The Cost Function Search tries to find relevant sub-goals to solve the problem and the Flat approach tries to find the best low level actions by evaluating the performance of the found controller. In general it is expected that if more prior knowledge is used the algorithm learns faster and/or finds better results. The algorithms are compared using a simulator, where surprisingly the Cost Function Search method is found to result in controllers performing up to factor two better than the Min-Max approach and a factor four better that the flat approach. This indicates that care should be taken how prior knowledge is used as it not always leads to better results. Test on the robot show that the influence of repairs is significant and that the robot is able to learn to walk in straight line. It is believed that due to the back of the robot dragging on the floor it is unable to turn sharply and thus reach goals situated next to the robot. This in combination with frequent breakdowns made it impossible to test the complete algorithms on this version of the robot. Further test using an improved version of the robot need to be done to obtain conclusive results.

BMD

BioMechanical Engineering

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Jonker, P.P. (mentor), Caarls, W. (mentor).

Subjects/Keywords: learning; walking; robot; hexapod; reinforcement; prior knowledge

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

APA (6^th Edition):

Moerdijk, M. M. (. (2013). Learning to walk using minimum prior knowledge: And a small hexapod robot. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fafd5dd4-653c-4b39-965f-c48707a2c1ca

Chicago Manual of Style (16^th Edition):

Moerdijk, M M (author). “Learning to walk using minimum prior knowledge: And a small hexapod robot.” 2013. Masters Thesis, Delft University of Technology. Accessed April 11, 2021. http://resolver.tudelft.nl/uuid:fafd5dd4-653c-4b39-965f-c48707a2c1ca.

MLA Handbook (7^th Edition):

Moerdijk, M M (author). “Learning to walk using minimum prior knowledge: And a small hexapod robot.” 2013. Web. 11 Apr 2021.

Vancouver:

Moerdijk MM(. Learning to walk using minimum prior knowledge: And a small hexapod robot. [Internet] [Masters thesis]. Delft University of Technology; 2013. [cited 2021 Apr 11]. Available from: http://resolver.tudelft.nl/uuid:fafd5dd4-653c-4b39-965f-c48707a2c1ca.

Council of Science Editors:

Moerdijk MM(. Learning to walk using minimum prior knowledge: And a small hexapod robot. [Masters Thesis]. Delft University of Technology; 2013. Available from: http://resolver.tudelft.nl/uuid:fafd5dd4-653c-4b39-965f-c48707a2c1ca

18. Nogales, Chris Lorena. Robot Autonomous Fire Location using a Weighted Probability Algorithm.

Degree: MS, Computer Engineering, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/73360

► Finding a fire inside of a structure without knowing its conditions poses a dangerous threat to the safety of firefighters. As a result, robots are… (more)

Subjects/Keywords: autonomy; perception; machine learning; firefighting robot

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APA (6^th Edition):

Nogales, C. L. (2016). Robot Autonomous Fire Location using a Weighted Probability Algorithm. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73360

Chicago Manual of Style (16^th Edition):

Nogales, Chris Lorena. “Robot Autonomous Fire Location using a Weighted Probability Algorithm.” 2016. Masters Thesis, Virginia Tech. Accessed April 11, 2021. http://hdl.handle.net/10919/73360.

MLA Handbook (7^th Edition):

Nogales, Chris Lorena. “Robot Autonomous Fire Location using a Weighted Probability Algorithm.” 2016. Web. 11 Apr 2021.

Vancouver:

Nogales CL. Robot Autonomous Fire Location using a Weighted Probability Algorithm. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10919/73360.

Council of Science Editors:

Nogales CL. Robot Autonomous Fire Location using a Weighted Probability Algorithm. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73360

University of New South Wales

19. Sushkov, Oleg. Autonomous robot interaction and use of objects.

Degree: Computer Science & Engineering, 2015, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true

► This thesis is focused on the skills that must be performed by an autonomous robot to interact with objects in a complex environment. For a… (more)

▼ This thesis is focused on the skills that must be performed by an autonomous robot to interact with objects in a complex environment. For a robot to manipulate and interact effectively with an object it must first learn the appearance of the object, determine the shape of the object, be able to recognise and localise the object in the environment and determine the physical properties of the object. We consider each of these skills in turn.A new method of object recognition using local image feature matching has been developed that is more accurate than existing methods, as well as being more efficient in some circumstances. Next we developed a system that combines robot initiated object motion and long term image feature tracking to accurately extract object features from complex scene images. This allows a robot to learn to recognise previously unseen objects in the presence of clutter, noise and background motion. The object feature matching and segmentation methods are then combined with 3D reconstruction methods to determine the object’s shape. This is done by stitching together multiple views of the object.Physical properties (weight, friction, centre of mass, etc) are important factors in determining how a robot can use an object. However, unlike shape and appearance, these may be impossible to determine by passive observation. We have developed a method in which the robot performs experiments on the object, and uses the outcomes to update its knowledge of the object’s physical properties. To perform the most informative experiments, a physics simulator is used to internally rehearse each experiment and its potential outcomes. The outcome of each experiment, after being performed on the physical object, is input back into the simulator forming a hypothesis-experiment-refinement loop. In this way the robot effectively learns the internal properties of an object. Finally, the robot uses this knowledge to plan and carry out a simple task in which the object is used as a tool. Advisors/Committee Members: Sammut, Claude, Computer Science & Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: computer vision; robotics; machine learning; robot interaction

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

APA (6^th Edition):

Sushkov, O. (2015). Autonomous robot interaction and use of objects. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Sushkov, Oleg. “Autonomous robot interaction and use of objects.” 2015. Doctoral Dissertation, University of New South Wales. Accessed April 11, 2021. http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Sushkov, Oleg. “Autonomous robot interaction and use of objects.” 2015. Web. 11 Apr 2021.

Vancouver:

Sushkov O. Autonomous robot interaction and use of objects. [Internet] [Doctoral dissertation]. University of New South Wales; 2015. [cited 2021 Apr 11]. Available from: http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true.

Council of Science Editors:

Sushkov O. Autonomous robot interaction and use of objects. [Doctoral Dissertation]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true

Oklahoma State University

20. Roy, Sayanti. Mutual reinforcement learning to improve robots as trainers.

Degree: Computer Science, 2020, Oklahoma State University

URL: http://hdl.handle.net/11244/325490

► Recently, collaborative robots have begun to train humans to achieve complex tasks, and the mutual information exchange between them can lead to successful robot-human collaborations.… (more)

Subjects/Keywords: human robot interaction; pedagogy; reinforcement learning

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

APA (6^th Edition):

Roy, S. (2020). Mutual reinforcement learning to improve robots as trainers. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/325490

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):

Roy, Sayanti. “Mutual reinforcement learning to improve robots as trainers.” 2020. Thesis, Oklahoma State University. Accessed April 11, 2021. http://hdl.handle.net/11244/325490.

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

MLA Handbook (7^th Edition):

Roy, Sayanti. “Mutual reinforcement learning to improve robots as trainers.” 2020. Web. 11 Apr 2021.

Vancouver:

Roy S. Mutual reinforcement learning to improve robots as trainers. [Internet] [Thesis]. Oklahoma State University; 2020. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/11244/325490.

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

Council of Science Editors:

Roy S. Mutual reinforcement learning to improve robots as trainers. [Thesis]. Oklahoma State University; 2020. Available from: http://hdl.handle.net/11244/325490

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

Georgia Tech

21. Hermans, Tucker Ryer. Representing and learning affordance-based behaviors.

Degree: PhD, Interactive Computing, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/51835

► Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot… (more)

▼ Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot to operate effectively in such a setting and not require excessive training from a human operator, it should be capable of discovering how to reliably manipulate novel objects it encounters. We characterize the possible methods by which a robot can act on an object using the concept of affordances. We define affordance-based behaviors as object manipulation strategies available to a robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate. This thesis concerns itself with developing the representation of these affordance- based behaviors along with associated learning algorithms. We identify three specific learning problems. The first asks which affordance-based behaviors a robot can successfully apply to a given object, including ones seen for the first time. Second, we examine how a robot can learn to best apply a specific behavior as a function of an object’s shape. Third, we investigate how learned affordance knowledge can be transferred between different objects and different behaviors. We claim that decomposing affordance-based behaviors into three separate factors— a control policy, a perceptual proxy, and a behavior primitive—aids an autonomous robot in learning to manipulate. Having a varied set of affordance-based behaviors available allows a robot to learn which behaviors perform most effectively as a function of an object’s identity or pose in the workspace. For a specific behavior a robot can use interactions with previously encountered objects to learn to robustly manipulate a novel object when first encountered. Finally, our factored representation allows a robot to transfer knowledge learned with one behavior to effectively manipulate an object in a qualitatively different manner by using a distinct controller or behavior primitive. We evaluate all work on a bimanual, mobile-manipulator robot. In all experiments the robot interacts with real-world objects sensed by an RGB-D camera. Advisors/Committee Members: Bobick, Aaron F. (advisor), Rehg, James M. (advisor), Christensen, Henrik I. (committee member), Stilman, Mike (committee member), Kemp, Charlie C. (committee member), Fox, Dieter (committee member).

Subjects/Keywords: Robot learning; Affordance learning; Autonomous robots; Machine learning

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APA (6^th Edition):

Hermans, T. R. (2014). Representing and learning affordance-based behaviors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51835

Chicago Manual of Style (16^th Edition):

Hermans, Tucker Ryer. “Representing and learning affordance-based behaviors.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 11, 2021. http://hdl.handle.net/1853/51835.

MLA Handbook (7^th Edition):

Hermans, Tucker Ryer. “Representing and learning affordance-based behaviors.” 2014. Web. 11 Apr 2021.

Vancouver:

Hermans TR. Representing and learning affordance-based behaviors. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1853/51835.

Council of Science Editors:

Hermans TR. Representing and learning affordance-based behaviors. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51835

Queensland University of Technology

22. Lehnert, Christopher. Locally weighted learning methods for non-rigid robot control.

Degree: 2015, Queensland University of Technology

URL: https://eprints.qut.edu.au/82358/

► This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration… (more)

Subjects/Keywords: learning robot control; learning; model predictive control; elastic joint robot; artificial intelligence; learning control; locally weighted learning; locally weighted regression; robot dynamics

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

APA (6^th Edition):

Lehnert, C. (2015). Locally weighted learning methods for non-rigid robot control. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/82358/

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):

Lehnert, Christopher. “Locally weighted learning methods for non-rigid robot control.” 2015. Thesis, Queensland University of Technology. Accessed April 11, 2021. https://eprints.qut.edu.au/82358/.

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

MLA Handbook (7^th Edition):

Lehnert, Christopher. “Locally weighted learning methods for non-rigid robot control.” 2015. Web. 11 Apr 2021.

Vancouver:

Lehnert C. Locally weighted learning methods for non-rigid robot control. [Internet] [Thesis]. Queensland University of Technology; 2015. [cited 2021 Apr 11]. Available from: https://eprints.qut.edu.au/82358/.

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

Council of Science Editors:

Lehnert C. Locally weighted learning methods for non-rigid robot control. [Thesis]. Queensland University of Technology; 2015. Available from: https://eprints.qut.edu.au/82358/

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

University of Michigan

23. Aladem, Mohamed D. Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving.

Degree: PhD, College of Engineering & Computer Science, 2020, University of Michigan

URL: http://hdl.handle.net/2027.42/153989

► Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving… (more)

▼ Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented. Advisors/Committee Members: Rawashdeh, Samir A. (advisor), Awad, Selim (committee member), Chehade, Abdallah (committee member), Mohammadi, Alireza (committee member).

Subjects/Keywords: Robot vision; Autonomous vehicles; Robot perception; Deep learning; Intelligent Systems and Robotics

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

APA (6^th Edition):

Aladem, M. D. (2020). Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153989

Chicago Manual of Style (16^th Edition):

Aladem, Mohamed D. “Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving.” 2020. Doctoral Dissertation, University of Michigan. Accessed April 11, 2021. http://hdl.handle.net/2027.42/153989.

MLA Handbook (7^th Edition):

Aladem, Mohamed D. “Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving.” 2020. Web. 11 Apr 2021.

Vancouver:

Aladem MD. Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/2027.42/153989.

Council of Science Editors:

Aladem MD. Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/153989

24. Spach, Michel. Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?.

Degree: Docteur es, Sciences de l'éducation, 2017, Sorbonne Paris Cité

URL: http://www.theses.fr/2017USPCB198

►

Cette recherche, qui s'inscrit dans le cadre des travaux en didactique de l'informatique de Baron et Bruillard, analyse la façon dont des enseignants du primaire,… (more)

▼

Cette recherche, qui s'inscrit dans le cadre des travaux en didactique de l'informatique de Baron et Bruillard, analyse la façon dont des enseignants du primaire, non experts en informatique,conçoivent et mettent en œuvre des scénarios impliquant des robots pédagogiques de sol dans leurs classes. La mise en œuvre de ces robots a été étudiée avec l'objectif, d'apporter un éclairage sur leurs possibles apports pédagogiques. Il s'est agi de préciser comment ces enseignants parviennent à définir des situations didactiques de ces objets de connaissances auxquels ils n'ont jamais été confrontés et d'analyser la manière dont ils parviennent à développer chez les élèves une pensée informatique en actes. L'activité des élèves a été analysée, au travers l'approche instrumentale (Rabardel), en vue de comprendre de quelle manière l'apprentissage de concepts en informatique émerge de ces activités. La question des apprentissages des concepts et méthodes propres au domaine informatique par le biais de la robotique est analysée en prenant appui sur la théorie des champs conceptuels (Vergnaud). Cette recherche apporte des éléments permettant de comprendre comment ces enseignants parviennent, de manière intuitive, à développer et à mettre en œuvre des scénarios pour enseigner quelques concepts informatiques. Elle témoigne de leur capacité à intégrer des objets tangibles ou symboliques dans des séances d'apprentissage en informatique, en procédant à une analyse préalable à minima du fonctionnement du robot. Au cours des activités dans lesquelles ils sont mobilisés, outils robotiques et aides pédagogiques accompagnent les apprentissages. Sur le plan des apprentissages, les élèves se sont forgés, par des démarches d'instrumentation et d'instrumentalisation, des instruments et des méthodes pour comprendre l'objet informatique. Les concepts et notions en jeu sont particulièrement dépendants des contextes technologiques spécifiques à chacun des robots. Des méthodes propres à la production logicielle ont permis le séquençage de l'activité de programmation en phases de spécification, conception, réalisation et mise au point. Des paradigmes de programmation ont aussi été approchés, comme la programmation procédurale dans le cas du robot Bee-Bot et la programmation événementielle dans le cas de l'étude du comportement du robot Thymio. En dehors du domaine informatique, la résolution de problème, en étant placée au cœur des scénarios, a permis aux élèves de développer des démarches de tâtonnements, d'essais-erreurs dans un contexte de travail en petit groupe favorisant les échanges et les interactions entre les élèves.

This research, which takes place within the framework of Baron and Bruillard's research in didactics of computer science,analyzes how primary school teachers, not computer experts, design and implement scenarios involving ground pedagogical robots in their classrooms. The integration of these robots has been studied with the aim of shedding light on their possible pedagogical contributions. It shows how these teachers succeed…

Advisors/Committee Members: Baron, Georges-Louis (thesis director).

Subjects/Keywords: Informatique; Programmation; Robot; Apprentissage; Concept; Computer science; Programming; Robot; Learning; Concept; 371.334

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

APA (6^th Edition):

Spach, M. (2017). Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2017USPCB198

Chicago Manual of Style (16^th Edition):

Spach, Michel. “Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?.” 2017. Doctoral Dissertation, Sorbonne Paris Cité. Accessed April 11, 2021. http://www.theses.fr/2017USPCB198.

MLA Handbook (7^th Edition):

Spach, Michel. “Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?.” 2017. Web. 11 Apr 2021.

Vancouver:

Spach M. Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2017. [cited 2021 Apr 11]. Available from: http://www.theses.fr/2017USPCB198.

Council of Science Editors:

Spach M. Activités robotiques à l'école primaire et apprentissage de concepts informatiques : quelle place du scénario pédagogique ? Les limites du co-apprentissage : Robotic activities in primary school and learning computer concepts : what is the role of the pedagogical scenario?. [Doctoral Dissertation]. Sorbonne Paris Cité; 2017. Available from: http://www.theses.fr/2017USPCB198

KTH

25. Lau, Cidney. Support Vector Machine Algorithm applied to Industrial Robot Error Recovery.

Degree: Computer Science and Communication (CSC), 2015, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331

►

A Machine Learning approach for error recovery in an industrial robot for the plastic mold industry isproposed in this master thesis project. The goal… (more)

▼

A Machine Learning approach for error recovery in an industrial robot for the plastic mold industry isproposed in this master thesis project. The goal was to improve the present error recovery method byproviding a learning algorithm to the system instead of using the traditional algorithm-based control.The chosen method was the Support Vector Machine (SVM) due to the robustness and the goodgeneralization performance in real-world applications. Furthermore, SVM generates good classifierseven with a minimal number of training examples. In production, there will be no need for a humanoperator to train the SVM with hundreds or thousands of training examples to achieve goodgeneralization. The advantage with SVM is that good accuracy can be achieved with only a couple oftraining examples if the training examples are well designed.Firstly, the algorithm proposed was evaluated experimentally. The experiments consisted of correcthandling of classification performance on training examples, which was a hand-coded data set createdwith defined in- and output signals. Secondly, the results from the experiments were tested in asimulated environment. By using only a few training examples the SVM reached perfect performance.In conclusion, SVM is a good tool for classification and a suitable method for error recovery on theindustrial robot for the plastic mold industry.

En maskininlärningsstrategi för felhantering på industrirobotar inom plastformindustrin presenteras idetta examensarbete. Målet var att förbättra den nuvarande felhanteringen genom att applicera eninlärningsalgoritm istället för det traditionella förprogrammerade systemet till roboten. Den valdametoden är Support Vector Machine (SVM), då SVM är en robust metod som ger bra prestanda iverkliga tillämpningar. SVM genererar bra klassificerare även med ett minimalt antal träningsexempel.Fördelen med SVM är att god precision kan uppnås med bara ett par träningsexempel förutsatt attträningsexemplen är väldesignade. Detta betyder att operatörerna i produktionen inte behöver tränahundratals eller tusentals träningsexempel med SVM för att uppnå en god generalisering.I projektet utvärderasdes SVM metoden experimentellt varefter den testades i ett simuleringsprogram.Resultatet visade att SVM metoden gav en perfekt precision med hjälp av endast ett fåtal träningsdata.En slutsats från denna studie är att SVM är en bra metod för klassificering och lämplig för felhanteringpå industrirobotar inom plastindustrin.

Subjects/Keywords: Support Vector Machine; SVM; Machine Learning; industrial robot; robot; error recovery; Computer Sciences; Datavetenskap (datalogi)

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

APA (6^th Edition):

Lau, C. (2015). Support Vector Machine Algorithm applied to Industrial Robot Error Recovery. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331

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):

Lau, Cidney. “Support Vector Machine Algorithm applied to Industrial Robot Error Recovery.” 2015. Thesis, KTH. Accessed April 11, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331.

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

MLA Handbook (7^th Edition):

Lau, Cidney. “Support Vector Machine Algorithm applied to Industrial Robot Error Recovery.” 2015. Web. 11 Apr 2021.

Vancouver:

Lau C. Support Vector Machine Algorithm applied to Industrial Robot Error Recovery. [Internet] [Thesis]. KTH; 2015. [cited 2021 Apr 11]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331.

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

Council of Science Editors:

Lau C. Support Vector Machine Algorithm applied to Industrial Robot Error Recovery. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331

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

Colorado School of Mines

26. Liu, Rui. Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A.

Degree: PhD, Mechanical Engineering, 2018, Colorado School of Mines

URL: http://hdl.handle.net/11124/172274

► Human-centered environment, which is defined by robots, human, and environmental conditions, is a key part of robot task executions. Accurate understanding of human-centered environment is… (more)

▼ Human-centered environment, which is defined by robots, human, and environmental conditions, is a key part of robot task executions. Accurate understanding of human-centered environment is the precondition of successful robot executions in real-world situations. However, in practical situations, there are a lot of environment uncertainties, such as task execution dynamics, tool/human user varieties, temporal/spatial limitations and scenario unstructured characteristics. Robot task execution performances have been largely undermined when robot task execution goes from controlled lab environments to uncontrolled practical environments. To improve robot execution performances in practical human-centered environments, in this dissertation, a three-layer cognitive framework is designed to support comprehensive robot understandings for dealing with environment uncertainties, making robot to “think” like a human, instead of merely to “act” like a human. With the cognitive comprehension framework, mainly three contributions have been made: 1). by abstracting low-level executions and real-world observations of human behaviors, robot behaviors, and environment conditions, high-level cognitive understanding is generated from a human perspective, endowing robots with abstract understanding of human-centered situations, 2). by flexibly decomposing a high-level abstract goal into low-level execution details, robots are able to flexibly make plans and revise plans according to human requirements and environment condition limitations, and 3). the three-layer cognitive framework is updated and evolved as more robot commonsense knowledge is learned. In this dissertation research, this framework is cooperated with efficient robot knowledge learning methods, such as web-mining supported knowledge collection and learning from demonstrations, supporting adaptive robot executions with different domain knowledge. Advisors/Committee Members: Zhang, Xiaoli (advisor), Zhang, Hao (committee member), King, Jeffrey C. (committee member), Stebner, Aaron P. (committee member).

Subjects/Keywords: cognitive robotics; environment adaptation; robot learning; decision making; artificial intelligence; human-robot interaction

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

APA (6^th Edition):

Liu, R. (2018). Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172274

Chicago Manual of Style (16^th Edition):

Liu, Rui. “Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A.” 2018. Doctoral Dissertation, Colorado School of Mines. Accessed April 11, 2021. http://hdl.handle.net/11124/172274.

MLA Handbook (7^th Edition):

Liu, Rui. “Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A.” 2018. Web. 11 Apr 2021.

Vancouver:

Liu R. Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2018. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/11124/172274.

Council of Science Editors:

Liu R. Cognitive comprehension framework for human-centered situation learning and adaptation in robotics, A. [Doctoral Dissertation]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172274

27. Mayran de Chamisso, Fabrice. Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies.

Degree: Docteur es, Robotique, 2016, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2016SACLS413

►

Il est fondamental pour un robot d'être capable de se déplacer de manière complètement autonome afin d'accomplir une mission qui lui a été confiée, et… (more)

▼

Il est fondamental pour un robot d'être capable de se déplacer de manière complètement autonome afin d'accomplir une mission qui lui a été confiée, et ce avec un budget énergétique fini, dans un laps de temps contraint et sans connaissances préalables de l’environnement. Afin d'atteindre un objectif dans le plan ou l'espace, un robot doit à minima être capable d'accomplir quatre tâches: maintenir une représentation abstraite de l'environnement (une carte), être capable de se localiser à l'intérieur de cette représentation, utiliser la représentation pour planifier des itinéraires et naviguer le long de la trajectoire prévue tout en s'adaptant aux dynamiques de l'environnement et en évitant les obstacles. Chacun de ces problèmes a été étudié par la communauté de la robotique. Cependant, ces quatre composants sont en général étudiés séparément et sont par conséquent incompatibles entre eux pour l'essentiel. De plus, étant donné qu'humains et robots ne disposent que de ressources computationelles et mémorielles finies, les algorithmes de planification, navigation et SLAM devraient être capables de fonctionner avec des données incomplètes ou compressées tout en garantissant que le ou les objectifs fixés soient atteints. Dans cette thèse, la planification, la navigation et le SLAM dans des environnements arbitrairement grands et avec des ressources computationelles et mémorielles finies sont vues comme un seul problème, créant un nouveau paradigme que nous appelons Navigation Exploratoire au long de la Vie ou Lifelong Exploratory Navigation.

One of the yet unresolved canonical problems of robotics is to have robots move completely autonomously in order to accomplish any mission they are charged with, with time and resource constraints and without prior knowledge of the environment. Reaching a goal requires the robot to perform at least four tasks: maintaining an abstract representation of the environment (map), being able to localize itself within this representation, using the representation to plan paths and navigating on the planned paths while handling dynamics of the environment and avoiding obstacles. Each of these problems has been studied extensively by the robotics community. However, the four components are usually studied separately, and as a result are mostly incompatible with each other. Additionally, since humans as well as robots have to operate with finite memory and computing resources, long running planning, navigation and SLAM algorithms may have to operate on incomplete or compressed data while guaranteeing that the goal(s) can still be reached. In this thesis, planning, navigation and SLAM in arbitrarily large environments with finite computing resources and memory are considered as one single problem, for a new bio-inspired paradigm which we call Lifelong Exploratory Navigation.

Advisors/Committee Members: Aupetit, Michaël (thesis director).

Subjects/Keywords: Robot; Autonome; Intelligence; Apprentissage; Slam; Planification; Robot; Autonomous; Intelligence; Learning; Slam; Planning

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

APA (6^th Edition):

Mayran de Chamisso, F. (2016). Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLS413

Chicago Manual of Style (16^th Edition):

Mayran de Chamisso, Fabrice. “Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed April 11, 2021. http://www.theses.fr/2016SACLS413.

MLA Handbook (7^th Edition):

Mayran de Chamisso, Fabrice. “Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies.” 2016. Web. 11 Apr 2021.

Vancouver:

Mayran de Chamisso F. Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2021 Apr 11]. Available from: http://www.theses.fr/2016SACLS413.

Council of Science Editors:

Mayran de Chamisso F. Lifelong Exploratory Navigation : integrating planning, navigation and SLAM for autonomous mobile robots with finite resources : Navigation exploratoire au long de la vie : une approche intégrant planification, navigation, cartographie et localisation pour des robots mobiles disposant de ressources finies. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLS413

NSYSU

28. Hung, I-chun. Developing Ubiquitous Learning System with Robot for Children's Learning.

Degree: Master, Information Management, 2009, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710109-004149

► An advanced architecture of learning system with flexible, mobile and joyful features for supporting ubiquitous learning is developed in this research. The architecture consists of… (more)

▼ An advanced architecture of learning system with flexible, mobile and joyful features for supporting ubiquitous learning is developed in this research. The architecture consists of five hardware key elements and a supporting information system to form a brand-new ubiquitous learning system. We call the designed and developed system as Ubiquitous Open-structured Neo-tech Edutainment (or u-ONE System for short) which includes learning robot, sensing input device, mobile computing device, mobile output device, wireless local network and u-ONE Software. The design and development of u-ONE System is guided by experiential learning theory, constructivism learning theory, and joyful learning element. Instruction, collaboration learning and self-learning of application modes are supported by u-ONE Software for realizing ubiquitous learning. The aim of this research is to design and develop a prototype of u-ONE System includes hardware and software components for supporting childrenâs learning by using robot and RFID. Instructor and learners can meet at any place with their own gears to form a u-ONE System and start instruction and/or learning activities. Only instructors need to operate the control station for coordination; learners just intuitively interact with learning robot by a natural and person-to-person-liked interaction method. In u-ONE System, learners do not need to have good information technologies literacy such as the keyboarding skills which are especially crucial for the earlier childhood learners. Besides, many parents and educators are concerned of watching computer screen for a long time that may harm childrenâs eyesight; u-ONE System provides an alternative solution for this. This researchâs experiment result found most learners could arouse their learning motivations and help them concentrate on learning activities. The class order is also improved for instructors more easily to control the behaviors of learners during the class. Advisors/Committee Members: Yueh-Min Huang (chair), Nian-Shing Chen (committee member), Gwo-Jen Hwang (chair).

Subjects/Keywords: u-ONE; Joyful learning; RFID; Robot; Ubiquitous learning

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

APA (6^th Edition):

Hung, I. (2009). Developing Ubiquitous Learning System with Robot for Children's Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710109-004149

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):

Hung, I-chun. “Developing Ubiquitous Learning System with Robot for Children's Learning.” 2009. Thesis, NSYSU. Accessed April 11, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710109-004149.

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

MLA Handbook (7^th Edition):

Hung, I-chun. “Developing Ubiquitous Learning System with Robot for Children's Learning.” 2009. Web. 11 Apr 2021.

Vancouver:

Hung I. Developing Ubiquitous Learning System with Robot for Children's Learning. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Apr 11]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710109-004149.

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

Council of Science Editors:

Hung I. Developing Ubiquitous Learning System with Robot for Children's Learning. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710109-004149

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

NSYSU

29. Lee, Jia-Lin. Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning.

Degree: Master, Electrical Engineering, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0619117-151814

► The main objective of this thesis is to design an intelligent gain controller for a robot arm based on reinforcement learning methods. The controller is… (more)

Subjects/Keywords: Visual servoing; Robot arm; Q-learning; Reinforcement learning

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

APA (6^th Edition):

Lee, J. (2017). Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0619117-151814

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):

Lee, Jia-Lin. “Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning.” 2017. Thesis, NSYSU. Accessed April 11, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0619117-151814.

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

MLA Handbook (7^th Edition):

Lee, Jia-Lin. “Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning.” 2017. Web. 11 Apr 2021.

Vancouver:

Lee J. Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Apr 11]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0619117-151814.

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

Council of Science Editors:

Lee J. Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0619117-151814

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

University of California – Berkeley

30. Lin, Chung-Yen. Learning of Task-specific Control Policies for Industrial Robots.

Degree: Mechanical Engineering, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/51q7s5c1

► Today's industrial robots are designed to be able to execute versatile tasks like a human. When deploying the robots to production lines, however, they only… (more)

Subjects/Keywords: Robotics; Control; Learning Control; Machine Learning; Optimization; Robot

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Lin, C. (2016). Learning of Task-specific Control Policies for Industrial Robots. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/51q7s5c1

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):

Lin, Chung-Yen. “Learning of Task-specific Control Policies for Industrial Robots.” 2016. Thesis, University of California – Berkeley. Accessed April 11, 2021. http://www.escholarship.org/uc/item/51q7s5c1.

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

MLA Handbook (7^th Edition):

Lin, Chung-Yen. “Learning of Task-specific Control Policies for Industrial Robots.” 2016. Web. 11 Apr 2021.

Vancouver:

Lin C. Learning of Task-specific Control Policies for Industrial Robots. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2021 Apr 11]. Available from: http://www.escholarship.org/uc/item/51q7s5c1.

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

Council of Science Editors:

Lin C. Learning of Task-specific Control Policies for Industrial Robots. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/51q7s5c1

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

Open Access Theses and Dissertations