Abstract:
This paper presents an algorithm for direct trajectory optimization in domains with contact. Since contacts and other unilateral constraints may introduce non-smooth dyna...Show MoreMetadata
Abstract:
This paper presents an algorithm for direct trajectory optimization in domains with contact. Since contacts and other unilateral constraints may introduce non-smooth dynamics, many standard algorithms of optimal control and reinforcement learning cannot be directly applied to such domains. We use a smooth contact model that can compute inverse dynamics through the contact, thereby avoiding hybrid representation of the non-smooth contact state. This allows us to formulate an unconstrained, continuous trajectory optimization problem, which can be solved using standard optimization tools. We demonstrate our approach by optimizing a running gait for a 31-dimensional simulated humanoid. The resulting gait is demonstrated in a movie attached as supplementary material. The optimization result exhibits a synchronous motion of the arm and the opposite leg, eliminating undesired angular momentum; this is a key feature of bipedal running, and its emergence attests to the power of the optimization process.
Date of Conference: 07-12 October 2012
Date Added to IEEE Xplore: 20 December 2012
ISBN Information: