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 highdimensional humanoid robots with little loss in efficiency.
Deisenroth et al. REF learn probabilistic forward models and use them to learn feedback controllers for generating walking policies on legged locomotion systems, including a real compass walker and a simulated biped.
Toward fast policy search for learning legged locomotion
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science