Main » Publications by Topic
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 | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
Control
| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
Learning Motor Primitives
| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
Robotics
| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
Human Motor Control
| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
Book Reviews
| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
| Abstract | The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm. |
| Notes | clmc |
| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.