A multi-stage approach for efficiently learning humanoid robot stand-up behavior

Abstract

Stand-up motion is among the most essential behaviors for humanoid robots. For achieving stable stand-up behavior, the traditional key-frame based motion planning methods are time-exhausted and expert knowledge dependent. On the other hand, classic trial-and-error based learning methods are inefficient due to the high degrees of freedom (DOFs) for humanoid robots and the difficulty in fixing appropriate reward functions. In this paper, a multi-stage learning approach is proposed to address the above issues. At the first stage, under a trajectory based motion control model, key motion frames sampled from human motion capture data (HMCD) are used for model initialization, through which the solution space could be pruned. At the second stage, the design of experiments (DOE) technique is introduced for fast and active searching in the pruned solution space. At the last stage, a refining process that adopts a stochastic gradient learning strategy is performed to achieve the final behavior. Under this three-stage learning framework, along with a simple heuristic reward function, the learning of the stand-up behavior for a kid-size humanoid robot is fulfilled successfully and efficiently.