Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IROS.2012.6385955 | IROS |
Keywords | Field | DocType |
adaptive control,biomechanics,feedback,humanoid robots,learning (artificial intelligence),learning systems,legged locomotion,biomechanically inspired biped,compass walker,controller design,fast policy search,gait policies learning,humanoid robots,learned locomotion models,learned probabilistic forward models,learning feedback controllers,legged locomotion systems,legged locomotive systems,legged robotics,mobility forms,reinforcement learning,walking policies | Data modeling,Compass,Gait,Computer science,Curse of dimensionality,Control engineering,Artificial intelligence,Probabilistic logic,Adaptive control,Robotics,Humanoid robot | Conference |
ISSN | Citations | PageRank |
2153-0858 | 10 | 0.58 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Peter Deisenroth | 1 | 1095 | 64.71 |
| Roberto Calandra | 2 | 105 | 13.42 |
| André Seyfarth | 3 | 195 | 29.57 |
| Jan Peters | 4 | 3553 | 264.28 |