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This list is automatically created, please see publications by year in order to have a more chronological overview on my publications. Note that the list on this page is automatically generated and as such always overlapping due to overlapping keywords.
Reinforcement Learning
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
Control
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
Learning Motor Primitives
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
Robotics
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
Human Motor Control
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
Book Reviews
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A first important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.