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 | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
Control
| Record Number | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
Learning Motor Primitives
| Record Number | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
Robotics
| Record Number | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
Human Motor Control
| Record Number | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
Book Reviews
| Record Number | 10188 |
| Reference Type | Journal Article |
| Author(s) | Peters, J. |
| Year | 2008 |
| Title | Machine Learning for Motor Skills in Robotics |
| Journal/Conference/Book Title | Künstliche Intelligenz |
| Keywords | motor control, motor primitives, motor learning |
| Abstract | Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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 and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning 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, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting. |
| Notes | jan |
| Number | 3 |
| Link to PDF | http://www-clmc.usc.edu/publications//P/Peters_KI_2008.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.