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 | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
Control
| Record Number | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
Learning Motor Primitives
| Record Number | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
Robotics
| Record Number | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
Human Motor Control
| Record Number | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
Book Reviews
| Record Number | 10136 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J., Schaal, S. |
| Year | 2007 |
| Title | Policy Learning for Motor Skills |
| Journal/Conference/Book Title | Proceedings of 14th International Conference on Neural Information Processing (ICONIP) |
| Keywords | Machine Learning, Reinforcement Learning, Robotics, Motor Primitives, Policy Gradients, Natural Actor-Critic, Reward-Weighted Regression |
| Abstract | Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution. |
| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications/P/peters_ICONIP2007.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.