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You searched for +publisher:"Delft University of Technology" +contributor:("Kober, Jens"). Showing records 1 – 30 of 34 total matches.

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Delft University of Technology

1. Krishnamoorthi, Sathish (author). Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.

Degree: 2018, Delft University of Technology

 Industrial robots can be found in automotive, food, chemical, and electronics industries. These robots are often caged and are secluded from human beings. A recent… (more)

Subjects/Keywords: Semi-parametric Gaussian Process Regression; Serial Manipulators; Gaussian process regression

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APA (6th Edition):

Krishnamoorthi, S. (. (2018). Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0

Chicago Manual of Style (16th Edition):

Krishnamoorthi, Sathish (author). “Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0.

MLA Handbook (7th Edition):

Krishnamoorthi, Sathish (author). “Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.” 2018. Web. 01 Dec 2020.

Vancouver:

Krishnamoorthi S(. Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0.

Council of Science Editors:

Krishnamoorthi S(. Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0


Delft University of Technology

2. Duan, Wuyang (author). Learning state representations for robotic control: Information disentangling and multi-modal learning.

Degree: 2017, Delft University of Technology

 Representation learning is a central topic in the field of deep learning. It aims at extracting useful state representations directly from raw data. In deep… (more)

Subjects/Keywords: State representation learning; model-based control; multi-modal learning; information disentangling

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APA (6th Edition):

Duan, W. (. (2017). Learning state representations for robotic control: Information disentangling and multi-modal learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:60b241e8-8b92-4dff-a7e7-e5ad40b62ee1

Chicago Manual of Style (16th Edition):

Duan, Wuyang (author). “Learning state representations for robotic control: Information disentangling and multi-modal learning.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:60b241e8-8b92-4dff-a7e7-e5ad40b62ee1.

MLA Handbook (7th Edition):

Duan, Wuyang (author). “Learning state representations for robotic control: Information disentangling and multi-modal learning.” 2017. Web. 01 Dec 2020.

Vancouver:

Duan W(. Learning state representations for robotic control: Information disentangling and multi-modal learning. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:60b241e8-8b92-4dff-a7e7-e5ad40b62ee1.

Council of Science Editors:

Duan W(. Learning state representations for robotic control: Information disentangling and multi-modal learning. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:60b241e8-8b92-4dff-a7e7-e5ad40b62ee1


Delft University of Technology

3. Keulen, Bart (author). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.

Degree: 2018, Delft University of Technology

 An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a… (more)

Subjects/Keywords: Reinforcement Learning; Exploration

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APA (6th Edition):

Keulen, B. (. (2018). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422

Chicago Manual of Style (16th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

MLA Handbook (7th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Web. 01 Dec 2020.

Vancouver:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

Council of Science Editors:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422


Delft University of Technology

4. de Zwart, Sjouke (author). Impact-Aware Learning from Demonstration.

Degree: 2019, Delft University of Technology

We often establish contact with our environment at non-zero speed. Grabbing and pushing objects without the need to stop our hands at the moment of… (more)

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APA (6th Edition):

de Zwart, S. (. (2019). Impact-Aware Learning from Demonstration. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c6f91fb2-2544-4802-bcda-4ee70ab0e2be

Chicago Manual of Style (16th Edition):

de Zwart, Sjouke (author). “Impact-Aware Learning from Demonstration.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:c6f91fb2-2544-4802-bcda-4ee70ab0e2be.

MLA Handbook (7th Edition):

de Zwart, Sjouke (author). “Impact-Aware Learning from Demonstration.” 2019. Web. 01 Dec 2020.

Vancouver:

de Zwart S(. Impact-Aware Learning from Demonstration. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:c6f91fb2-2544-4802-bcda-4ee70ab0e2be.

Council of Science Editors:

de Zwart S(. Impact-Aware Learning from Demonstration. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c6f91fb2-2544-4802-bcda-4ee70ab0e2be


Delft University of Technology

5. Beeftink, Mart (author). Learning kinematic models using a single tele-demonstration.

Degree: 2018, Delft University of Technology

 To successfully perform manipulation tasks in an unknown environment, a robot must be able to learn the kinematic constraints of the objects within this environment.… (more)

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APA (6th Edition):

Beeftink, M. (. (2018). Learning kinematic models using a single tele-demonstration. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:00b62422-54c4-4d4d-8c3f-1beebc398251

Chicago Manual of Style (16th Edition):

Beeftink, Mart (author). “Learning kinematic models using a single tele-demonstration.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:00b62422-54c4-4d4d-8c3f-1beebc398251.

MLA Handbook (7th Edition):

Beeftink, Mart (author). “Learning kinematic models using a single tele-demonstration.” 2018. Web. 01 Dec 2020.

Vancouver:

Beeftink M(. Learning kinematic models using a single tele-demonstration. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:00b62422-54c4-4d4d-8c3f-1beebc398251.

Council of Science Editors:

Beeftink M(. Learning kinematic models using a single tele-demonstration. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:00b62422-54c4-4d4d-8c3f-1beebc398251


Delft University of Technology

6. Guljelmović, Nikol (author). Task Parameter Inference in Human-Robot Interaction.

Degree: 2017, Delft University of Technology

Task-parameterized movement representation, as an approach for the generalization of demonstrations, is used to represent data from multiple local perspectives within the global reference frame,… (more)

Subjects/Keywords: Task-parameterized movement representation; Task parameter inference; human-robot interaction; Procrustes analysis

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APA (6th Edition):

Guljelmović, N. (. (2017). Task Parameter Inference in Human-Robot Interaction. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802

Chicago Manual of Style (16th Edition):

Guljelmović, Nikol (author). “Task Parameter Inference in Human-Robot Interaction.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802.

MLA Handbook (7th Edition):

Guljelmović, Nikol (author). “Task Parameter Inference in Human-Robot Interaction.” 2017. Web. 01 Dec 2020.

Vancouver:

Guljelmović N(. Task Parameter Inference in Human-Robot Interaction. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802.

Council of Science Editors:

Guljelmović N(. Task Parameter Inference in Human-Robot Interaction. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802


Delft University of Technology

7. Scholten, Jan (author). Deep Reinforcement Learning with Feedback-based Exploration.

Degree: 2019, Delft University of Technology

Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous… (more)

Subjects/Keywords: Reinforcement Learning; Artificial Intelligence; Machine Learning; Interactive Learning

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APA (6th Edition):

Scholten, J. (. (2019). Deep Reinforcement Learning with Feedback-based Exploration. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:acb6ea20-5e74-457e-84cd-56e33dd72979

Chicago Manual of Style (16th Edition):

Scholten, Jan (author). “Deep Reinforcement Learning with Feedback-based Exploration.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:acb6ea20-5e74-457e-84cd-56e33dd72979.

MLA Handbook (7th Edition):

Scholten, Jan (author). “Deep Reinforcement Learning with Feedback-based Exploration.” 2019. Web. 01 Dec 2020.

Vancouver:

Scholten J(. Deep Reinforcement Learning with Feedback-based Exploration. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:acb6ea20-5e74-457e-84cd-56e33dd72979.

Council of Science Editors:

Scholten J(. Deep Reinforcement Learning with Feedback-based Exploration. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:acb6ea20-5e74-457e-84cd-56e33dd72979


Delft University of Technology

8. Jacobs, Olav (author). Cooperative Robot Manipulators for Parcel picking and placing.

Degree: 2020, Delft University of Technology

In this thesis, a control scheme for lifting parcels using two robot manipulators is presented. The robots do not have a rigid grasp on the… (more)

Subjects/Keywords: Robot; Manipulator; Control; Adaptive Control

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APA (6th Edition):

Jacobs, O. (. (2020). Cooperative Robot Manipulators for Parcel picking and placing. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f6bc64b6-bc11-4c22-899c-1d2852e47efa

Chicago Manual of Style (16th Edition):

Jacobs, Olav (author). “Cooperative Robot Manipulators for Parcel picking and placing.” 2020. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:f6bc64b6-bc11-4c22-899c-1d2852e47efa.

MLA Handbook (7th Edition):

Jacobs, Olav (author). “Cooperative Robot Manipulators for Parcel picking and placing.” 2020. Web. 01 Dec 2020.

Vancouver:

Jacobs O(. Cooperative Robot Manipulators for Parcel picking and placing. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:f6bc64b6-bc11-4c22-899c-1d2852e47efa.

Council of Science Editors:

Jacobs O(. Cooperative Robot Manipulators for Parcel picking and placing. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f6bc64b6-bc11-4c22-899c-1d2852e47efa


Delft University of Technology

9. Irawan, Angga (author). Representing Symbolic Controllers with Deep Neural Networks.

Degree: 2018, Delft University of Technology

Controller synthesis techniques based on symbolic models or discrete abstractions are becoming increasingly attractive as they allow for synthesizing correct-by-design controllers of general nonlinear systems… (more)

Subjects/Keywords: Symbolic Controllers; Neural Networks; Correct-by-Design

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APA (6th Edition):

Irawan, A. (. (2018). Representing Symbolic Controllers with Deep Neural Networks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:677cdde8-b3fa-4fb4-8db2-3c87b03b5085

Chicago Manual of Style (16th Edition):

Irawan, Angga (author). “Representing Symbolic Controllers with Deep Neural Networks.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:677cdde8-b3fa-4fb4-8db2-3c87b03b5085.

MLA Handbook (7th Edition):

Irawan, Angga (author). “Representing Symbolic Controllers with Deep Neural Networks.” 2018. Web. 01 Dec 2020.

Vancouver:

Irawan A(. Representing Symbolic Controllers with Deep Neural Networks. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:677cdde8-b3fa-4fb4-8db2-3c87b03b5085.

Council of Science Editors:

Irawan A(. Representing Symbolic Controllers with Deep Neural Networks. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:677cdde8-b3fa-4fb4-8db2-3c87b03b5085


Delft University of Technology

10. Neogi, Sabyasachi (author). Automatic Tuning of Wind Tubrine controller.

Degree: 2017, Delft University of Technology

The energy demand in current times has increased greatly in last few years. This increasing demand calls for a sustainable and clean energy resource that… (more)

Subjects/Keywords: Gausssian Process Regression; Wind Turbine Control; monte carlo localization

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APA (6th Edition):

Neogi, S. (. (2017). Automatic Tuning of Wind Tubrine controller. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ba2c3967-a4b3-4a91-b15a-fa040aa1a87c

Chicago Manual of Style (16th Edition):

Neogi, Sabyasachi (author). “Automatic Tuning of Wind Tubrine controller.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:ba2c3967-a4b3-4a91-b15a-fa040aa1a87c.

MLA Handbook (7th Edition):

Neogi, Sabyasachi (author). “Automatic Tuning of Wind Tubrine controller.” 2017. Web. 01 Dec 2020.

Vancouver:

Neogi S(. Automatic Tuning of Wind Tubrine controller. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:ba2c3967-a4b3-4a91-b15a-fa040aa1a87c.

Council of Science Editors:

Neogi S(. Automatic Tuning of Wind Tubrine controller. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:ba2c3967-a4b3-4a91-b15a-fa040aa1a87c


Delft University of Technology

11. Hoeba, Nirul (author). VR Mediated Teleoperation: Total workspace utilization using null-space projection control.

Degree: 2019, Delft University of Technology

Joint limits and singularities limit the total and intuitive utilization of the robotic workspace in VR mediated teleoperation. This paper presents the development and validation… (more)

Subjects/Keywords: Teleoperation; Virtual Reality; Robot arm control; null-space control; end-effector control

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APA (6th Edition):

Hoeba, N. (. (2019). VR Mediated Teleoperation: Total workspace utilization using null-space projection control. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:31afa88f-1829-4f64-be5a-c099f4bfce8d

Chicago Manual of Style (16th Edition):

Hoeba, Nirul (author). “VR Mediated Teleoperation: Total workspace utilization using null-space projection control.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:31afa88f-1829-4f64-be5a-c099f4bfce8d.

MLA Handbook (7th Edition):

Hoeba, Nirul (author). “VR Mediated Teleoperation: Total workspace utilization using null-space projection control.” 2019. Web. 01 Dec 2020.

Vancouver:

Hoeba N(. VR Mediated Teleoperation: Total workspace utilization using null-space projection control. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:31afa88f-1829-4f64-be5a-c099f4bfce8d.

Council of Science Editors:

Hoeba N(. VR Mediated Teleoperation: Total workspace utilization using null-space projection control. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:31afa88f-1829-4f64-be5a-c099f4bfce8d


Delft University of Technology

12. Hjartarson, Daníel (author). Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data.

Degree: 2019, Delft University of Technology

Multivariate images are built up by measuring multiple features or variables simultaneously while recording a measurement’s location. An example of such images is Imaging Mass… (more)

Subjects/Keywords: IMS; MAF; Geostatistics; Factorization; Linear transformations

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APA (6th Edition):

Hjartarson, D. (. (2019). Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:50cacbe5-1a02-4980-a916-900e07f52461

Chicago Manual of Style (16th Edition):

Hjartarson, Daníel (author). “Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:50cacbe5-1a02-4980-a916-900e07f52461.

MLA Handbook (7th Edition):

Hjartarson, Daníel (author). “Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data.” 2019. Web. 01 Dec 2020.

Vancouver:

Hjartarson D(. Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:50cacbe5-1a02-4980-a916-900e07f52461.

Council of Science Editors:

Hjartarson D(. Extension of Maximum Autocorrelation Factorization: With application to imaging mass spectrometry data. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:50cacbe5-1a02-4980-a916-900e07f52461


Delft University of Technology

13. Chawla, Hemang (author). Robot Placement for Mobile Manipulation in Domestic Environments.

Degree: 2017, Delft University of Technology

The development of domestic mobile manipulators for unconstrained environments has driven significant research recently. Robot Care Systems has been pioneering in developing a prototype of… (more)

Subjects/Keywords: Mobile Manipulation; Robot; Robot Placement; Vision; Domestic Environments; Commutation Configuration; Robotics; Base Placement; Reachability; Workspace Analysis; Optimization; Planability; Algorithm

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APA (6th Edition):

Chawla, H. (. (2017). Robot Placement for Mobile Manipulation in Domestic Environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e095b2be-c497-4de4-a55b-25126e960dbe

Chicago Manual of Style (16th Edition):

Chawla, Hemang (author). “Robot Placement for Mobile Manipulation in Domestic Environments.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:e095b2be-c497-4de4-a55b-25126e960dbe.

MLA Handbook (7th Edition):

Chawla, Hemang (author). “Robot Placement for Mobile Manipulation in Domestic Environments.” 2017. Web. 01 Dec 2020.

Vancouver:

Chawla H(. Robot Placement for Mobile Manipulation in Domestic Environments. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:e095b2be-c497-4de4-a55b-25126e960dbe.

Council of Science Editors:

Chawla H(. Robot Placement for Mobile Manipulation in Domestic Environments. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:e095b2be-c497-4de4-a55b-25126e960dbe


Delft University of Technology

14. Duan, Wen Jie (author). Suction Grasp Pose Planning Using Self-supervision and Transfer Learning.

Degree: 2018, Delft University of Technology

Planning grasp poses for a robot on unknown objects in cluttered environments is still an open problem. Recent research suggests that deep learning technique is… (more)

Subjects/Keywords: Grasping; Deep Learning; Transfer learning; Self-supervision; Robot

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APA (6th Edition):

Duan, W. J. (. (2018). Suction Grasp Pose Planning Using Self-supervision and Transfer Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f41bde0e-b9c9-46de-a52c-fc3b2885b850

Chicago Manual of Style (16th Edition):

Duan, Wen Jie (author). “Suction Grasp Pose Planning Using Self-supervision and Transfer Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:f41bde0e-b9c9-46de-a52c-fc3b2885b850.

MLA Handbook (7th Edition):

Duan, Wen Jie (author). “Suction Grasp Pose Planning Using Self-supervision and Transfer Learning.” 2018. Web. 01 Dec 2020.

Vancouver:

Duan WJ(. Suction Grasp Pose Planning Using Self-supervision and Transfer Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:f41bde0e-b9c9-46de-a52c-fc3b2885b850.

Council of Science Editors:

Duan WJ(. Suction Grasp Pose Planning Using Self-supervision and Transfer Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f41bde0e-b9c9-46de-a52c-fc3b2885b850


Delft University of Technology

15. van der Heijden, Bas (author). Iterative Bias Estimation for an Ultra-Wideband Localization System.

Degree: 2019, Delft University of Technology

Three bias estimation frameworks are presented that mitigate position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that… (more)

Subjects/Keywords: Ultra-wideband technology; adaptive observer design; Bayesian methods; sensor fusion; recursive least squares; classification; non line-of-sight

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APA (6th Edition):

van der Heijden, B. (. (2019). Iterative Bias Estimation for an Ultra-Wideband Localization System. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fd9de39b-2f6b-45d7-94a6-5b5754497a61

Chicago Manual of Style (16th Edition):

van der Heijden, Bas (author). “Iterative Bias Estimation for an Ultra-Wideband Localization System.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:fd9de39b-2f6b-45d7-94a6-5b5754497a61.

MLA Handbook (7th Edition):

van der Heijden, Bas (author). “Iterative Bias Estimation for an Ultra-Wideband Localization System.” 2019. Web. 01 Dec 2020.

Vancouver:

van der Heijden B(. Iterative Bias Estimation for an Ultra-Wideband Localization System. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:fd9de39b-2f6b-45d7-94a6-5b5754497a61.

Council of Science Editors:

van der Heijden B(. Iterative Bias Estimation for an Ultra-Wideband Localization System. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:fd9de39b-2f6b-45d7-94a6-5b5754497a61


Delft University of Technology

16. Radojević, Jovana (author). Cooperative Visual Object Learning.

Degree: 2017, Delft University of Technology

 A lot of attention has recently been focused on possible benefits of the cooperation between machines and humans. Taking the best from machines and humans… (more)

Subjects/Keywords: selv-evaluation of classifiers; object recognition; Object learning; Visual System; cooperative object learning

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APA (6th Edition):

Radojević, J. (. (2017). Cooperative Visual Object Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:90383567-fc0d-4775-bb52-613b7074a676

Chicago Manual of Style (16th Edition):

Radojević, Jovana (author). “Cooperative Visual Object Learning.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:90383567-fc0d-4775-bb52-613b7074a676.

MLA Handbook (7th Edition):

Radojević, Jovana (author). “Cooperative Visual Object Learning.” 2017. Web. 01 Dec 2020.

Vancouver:

Radojević J(. Cooperative Visual Object Learning. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:90383567-fc0d-4775-bb52-613b7074a676.

Council of Science Editors:

Radojević J(. Cooperative Visual Object Learning. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:90383567-fc0d-4775-bb52-613b7074a676


Delft University of Technology

17. van Bekkum, Rob (author). Learning and Optimizing Probabilistic Models for Planning under Uncertainty.

Degree: 2017, Delft University of Technology

Decision-theoretic planning techniques are increasingly being used to obtain (optimal) plans for domains involving uncertainty, which may be present in the form of the controlling… (more)

Subjects/Keywords: planning under uncertainty; Bayesian Optimization; probabilistic model learning; Markov Decision Processes; decision-theoretic planning

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APA (6th Edition):

van Bekkum, R. (. (2017). Learning and Optimizing Probabilistic Models for Planning under Uncertainty. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:37e80be9-ab78-427b-b317-c5529a752d7d

Chicago Manual of Style (16th Edition):

van Bekkum, Rob (author). “Learning and Optimizing Probabilistic Models for Planning under Uncertainty.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:37e80be9-ab78-427b-b317-c5529a752d7d.

MLA Handbook (7th Edition):

van Bekkum, Rob (author). “Learning and Optimizing Probabilistic Models for Planning under Uncertainty.” 2017. Web. 01 Dec 2020.

Vancouver:

van Bekkum R(. Learning and Optimizing Probabilistic Models for Planning under Uncertainty. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:37e80be9-ab78-427b-b317-c5529a752d7d.

Council of Science Editors:

van Bekkum R(. Learning and Optimizing Probabilistic Models for Planning under Uncertainty. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:37e80be9-ab78-427b-b317-c5529a752d7d


Delft University of Technology

18. Wout, Daan (author). Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.

Degree: 2019, Delft University of Technology

A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is… (more)

Subjects/Keywords: Machine Learning; Interactive Learning; Gaussian Process; Regression; Feedback

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APA (6th Edition):

Wout, D. (. (2019). Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a

Chicago Manual of Style (16th Edition):

Wout, Daan (author). “Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a.

MLA Handbook (7th Edition):

Wout, Daan (author). “Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.” 2019. Web. 01 Dec 2020.

Vancouver:

Wout D(. Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a.

Council of Science Editors:

Wout D(. Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a


Delft University of Technology

19. van der Arend, Dennis (author). Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning.

Degree: 2018, Delft University of Technology

Traditionally a wind turbine’s power curve is used to model the long-term energy yield of the wind turbine and afterwards assess the performance of the… (more)

Subjects/Keywords: Wind Energy; Power curve; Machine Learning; Neural Networks

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APA (6th Edition):

van der Arend, D. (. (2018). Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b0e50a76-351e-4678-82ec-5c5868e8fc88

Chicago Manual of Style (16th Edition):

van der Arend, Dennis (author). “Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:b0e50a76-351e-4678-82ec-5c5868e8fc88.

MLA Handbook (7th Edition):

van der Arend, Dennis (author). “Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning.” 2018. Web. 01 Dec 2020.

Vancouver:

van der Arend D(. Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:b0e50a76-351e-4678-82ec-5c5868e8fc88.

Council of Science Editors:

van der Arend D(. Data-driven multivariate wind turbine performance modeling: Refining wind turbine performance estimations for atmospheric conditions by using machine learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:b0e50a76-351e-4678-82ec-5c5868e8fc88


Delft University of Technology

20. Wymenga, Jan (author). Weather Condition Estimation in Automated Vehicles.

Degree: 2018, Delft University of Technology

This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle… (more)

Subjects/Keywords: weather types; machine learning; intelligent vehicles; vision; driving; weather

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APA (6th Edition):

Wymenga, J. (. (2018). Weather Condition Estimation in Automated Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94

Chicago Manual of Style (16th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

MLA Handbook (7th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Web. 01 Dec 2020.

Vancouver:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

Council of Science Editors:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94


Delft University of Technology

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

Degree: 2019, Delft University of Technology

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 (6th 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 (16th 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 December 01, 2020. http://resolver.tudelft.nl/uuid:8ae08ee1-66e0-4eba-9158-ce7d94bf3a98.

MLA Handbook (7th Edition):

de Jong, Tobias (author). “The effect of sampling methods on Deep Q-Networks in robot navigation tasks.” 2019. Web. 01 Dec 2020.

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 2020 Dec 01]. 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

22. Greevink, Thijs (author). Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions.

Degree: 2019, Delft University of Technology

This thesis tests the hypothesis that distributional deep reinforcement learning (RL) algorithms get an increased performance over expectation based deep RL because of the regularizing… (more)

Subjects/Keywords: Deep Reinforcement Learning; QR-DQN; Distributional Reinforcement Learning; Prioritized Experience Replay; Wasserstein metric; Regularization

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APA (6th Edition):

Greevink, T. (. (2019). Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6397c8d3-c96f-490e-b3aa-2cb3ce447f4a

Chicago Manual of Style (16th Edition):

Greevink, Thijs (author). “Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:6397c8d3-c96f-490e-b3aa-2cb3ce447f4a.

MLA Handbook (7th Edition):

Greevink, Thijs (author). “Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions.” 2019. Web. 01 Dec 2020.

Vancouver:

Greevink T(. Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:6397c8d3-c96f-490e-b3aa-2cb3ce447f4a.

Council of Science Editors:

Greevink T(. Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:6397c8d3-c96f-490e-b3aa-2cb3ce447f4a


Delft University of Technology

23. Bos, Evert (author). Including traffic light recognition in general object detection with YOLOv2.

Degree: 2019, Delft University of Technology

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it… (more)

Subjects/Keywords: Traffic Light recognition; machine learning; YOLO; object detection; COCO; LISA

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APA (6th Edition):

Bos, E. (. (2019). Including traffic light recognition in general object detection with YOLOv2. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

Chicago Manual of Style (16th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

MLA Handbook (7th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Web. 01 Dec 2020.

Vancouver:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

Council of Science Editors:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03


Delft University of Technology

24. El Doori, Isa (author). Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation.

Degree: 2019, Delft University of Technology

The ability to locate specific objects within images is an essential step in various computer vision based engineering applications. Image segmentation is the task of… (more)

Subjects/Keywords: Deep Learning; RGB-D; Warehouse automation

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APA (6th Edition):

El Doori, I. (. (2019). Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d95c85ed-f96b-4956-ad17-e8e5194a3e8c

Chicago Manual of Style (16th Edition):

El Doori, Isa (author). “Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:d95c85ed-f96b-4956-ad17-e8e5194a3e8c.

MLA Handbook (7th Edition):

El Doori, Isa (author). “Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation.” 2019. Web. 01 Dec 2020.

Vancouver:

El Doori I(. Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:d95c85ed-f96b-4956-ad17-e8e5194a3e8c.

Council of Science Editors:

El Doori I(. Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d95c85ed-f96b-4956-ad17-e8e5194a3e8c


Delft University of Technology

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

Degree: 2020, Delft University of Technology

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)

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

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APA (6th 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 (16th 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 December 01, 2020. http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7.

MLA Handbook (7th Edition):

Jauhri, Snehal (author). “Interactive Learning in State-space: Enabling robots to learn from non-expert humans.” 2020. Web. 01 Dec 2020.

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 2020 Dec 01]. 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


Delft University of Technology

26. van Ramshorst, Arjan (author). Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.

Degree: 2018, Delft University of Technology

Knowledge on adversaries during military missions at sea heavily influences decision making, making identification of unknown vessels an important task. Identification of surrounding vessels based… (more)

Subjects/Keywords: Semantic Segmentation; Deep Learning; Data Augmentation

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APA (6th Edition):

van Ramshorst, A. (. (2018). Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b

Chicago Manual of Style (16th Edition):

van Ramshorst, Arjan (author). “Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b.

MLA Handbook (7th Edition):

van Ramshorst, Arjan (author). “Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.” 2018. Web. 01 Dec 2020.

Vancouver:

van Ramshorst A(. Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b.

Council of Science Editors:

van Ramshorst A(. Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b


Delft University of Technology

27. Valentini, Carlo (author). Online Reinforcement Learning Control of an Electromagnetic Manipulator.

Degree: 2019, Delft University of Technology

 Machine Learning Control is a control paradigm that applies Artificial Intelligence methods to control problems. Within this domain, the field of Reinforcement Learning (RL) is… (more)

Subjects/Keywords: Machine Learning Control; Reinforcement Learning; Magnetic manipulator; Magman

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APA (6th Edition):

Valentini, C. (. (2019). Online Reinforcement Learning Control of an Electromagnetic Manipulator. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0f6d733f-f66b-4313-b3a5-554457238812

Chicago Manual of Style (16th Edition):

Valentini, Carlo (author). “Online Reinforcement Learning Control of an Electromagnetic Manipulator.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:0f6d733f-f66b-4313-b3a5-554457238812.

MLA Handbook (7th Edition):

Valentini, Carlo (author). “Online Reinforcement Learning Control of an Electromagnetic Manipulator.” 2019. Web. 01 Dec 2020.

Vancouver:

Valentini C(. Online Reinforcement Learning Control of an Electromagnetic Manipulator. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:0f6d733f-f66b-4313-b3a5-554457238812.

Council of Science Editors:

Valentini C(. Online Reinforcement Learning Control of an Electromagnetic Manipulator. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:0f6d733f-f66b-4313-b3a5-554457238812


Delft University of Technology

28. Khan, Tiamur (author). Multi-frame deep learning models for action detection in surveillance videos.

Degree: 2019, Delft University of Technology

Visual surveillance technologies are increasingly being used to monitor public spaces. These technologies process the recordings of surveillance cameras. Such recordings contain depictions of human… (more)

Subjects/Keywords: Video processing; Action detection; Deep learning; Surveillance

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APA (6th Edition):

Khan, T. (. (2019). Multi-frame deep learning models for action detection in surveillance videos. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ca531228-239d-4c22-a1dc-94cd23991cdd

Chicago Manual of Style (16th Edition):

Khan, Tiamur (author). “Multi-frame deep learning models for action detection in surveillance videos.” 2019. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:ca531228-239d-4c22-a1dc-94cd23991cdd.

MLA Handbook (7th Edition):

Khan, Tiamur (author). “Multi-frame deep learning models for action detection in surveillance videos.” 2019. Web. 01 Dec 2020.

Vancouver:

Khan T(. Multi-frame deep learning models for action detection in surveillance videos. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:ca531228-239d-4c22-a1dc-94cd23991cdd.

Council of Science Editors:

Khan T(. Multi-frame deep learning models for action detection in surveillance videos. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:ca531228-239d-4c22-a1dc-94cd23991cdd


Delft University of Technology

29. Kokkalis, Konstantinos (author). LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.

Degree: 2018, Delft University of Technology

 Learning capabilities are a key requisite for an autonomous agent operating in dynamically changing and complex environments, where pre-programming is not anymore possible. Furthermore, it… (more)

Subjects/Keywords: Stability Analysis; Learning Control; LMI; ReLU; Deep neural networks; Linear Matrix Inequality; Locally Weighted Learning

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APA (6th Edition):

Kokkalis, K. (. (2018). LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47

Chicago Manual of Style (16th Edition):

Kokkalis, Konstantinos (author). “LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47.

MLA Handbook (7th Edition):

Kokkalis, Konstantinos (author). “LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.” 2018. Web. 01 Dec 2020.

Vancouver:

Kokkalis K(. LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47.

Council of Science Editors:

Kokkalis K(. LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47


Delft University of Technology

30. Rastogi, Divyam (author). Deep Reinforcement Learning for Bipedal Robots.

Degree: 2017, Delft University of Technology

 Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error.… (more)

Subjects/Keywords: Reinforcement Learning; Bipedal Walking; Deep neural networks; Model learning

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

APA (6th Edition):

Rastogi, D. (. (2017). Deep Reinforcement Learning for Bipedal Robots. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0fac495f-f87a-4a61-a80f-5f901323379a

Chicago Manual of Style (16th Edition):

Rastogi, Divyam (author). “Deep Reinforcement Learning for Bipedal Robots.” 2017. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:0fac495f-f87a-4a61-a80f-5f901323379a.

MLA Handbook (7th Edition):

Rastogi, Divyam (author). “Deep Reinforcement Learning for Bipedal Robots.” 2017. Web. 01 Dec 2020.

Vancouver:

Rastogi D(. Deep Reinforcement Learning for Bipedal Robots. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Dec 01]. Available from: http://resolver.tudelft.nl/uuid:0fac495f-f87a-4a61-a80f-5f901323379a.

Council of Science Editors:

Rastogi D(. Deep Reinforcement Learning for Bipedal Robots. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:0fac495f-f87a-4a61-a80f-5f901323379a

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