Toward fast policy search for learning legged locomotion
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Legged locomotion is one of the most versatile forms of mobility. However, despite the importance of legged locomotion and the large number of legged robotics studies, no biped or quadruped matches the agility and versatility of their biological counterparts to date. Approaches to designing controllers for legged locomotion systems are often based on either the assumption of perfectly known dynamics or mechanical designs that substantially reduce the dimensionality of the problem. The few existing approaches for learning controllers for legged systems either require exhaustive real-world data or they improve controllers only conservatively, leading to slow learning. We present a data-efficient approach to learning feedback controllers for legged locomotive systems, based on learned probabilistic forward models for generating walking policies. On a compass walker, we show that our approach allows for learning gait policies from very little data. Moreover, we analyze learned locomotion models of a biomechanically inspired biped. Our approach has the potential to scale to high-dimensional humanoid robots with little loss in efficiency.
Details
Original language | English |
---|---|
Title of host publication | 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012 |
Pages | 1787-1792 |
Number of pages | 6 |
Publication status | Published - 2012 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | IEEE International Conference on Intelligent Robots and Systems |
---|---|
ISSN | 2153-0858 |
Conference
Title | 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 |
---|---|
Duration | 7 - 12 October 2012 |
City | Vilamoura, Algarve |
Country | Portugal |
External IDs
ORCID | /0000-0001-9430-8433/work/158768047 |
---|