3 accepted papers
Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, current work commonly describes DRL as being a one-shot process, where the state, action and reward are assumed to be well defined and are directly used by an RL algorithm to obtain policies.
We apply fast online trajectory optimization for multi-step motion planning to Cassie, a bipedal robot designed to exploit natural spring-mass locomotion dynamics using lightweight, compliant legs. Our motion planning formulation simultaneously optimizes over center of mass motion, footholds, and ce…
Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions. In these model-based control strategies, the controller is often not fully aware of many details, includ…