Optimization of Vehicle Mounting Motions and Its Application to Full-Sized Humanoid, DRC-Hubo

Abstract

This paper describes optimization of humanoid’s whole body motion for vehicle mounting task. To accomplish the goal, a trajectory optimization framework based on the reinforcement learning agent is used in this study. The guideline input trajectory is planned and optimized as regards various dynamic and kinematic limitations of humanoid in the framework. In previous studies, the authors demonstrated test-and-evaluation process of the framework using a full-sized humanoid, Hubo+. Experimental testing however presented several problems like overheating and self-collisions. To resolve those issues and to validate the trajectory optimization approach, another humanoid, called DRC-Hubo, is newly designed. Keeping a main structure of the optimization framework, the cost value functions are revised to meet dynamic and kinematic changes. Experimental test and verification process using a simulation model and a physical prototype is demonstrated to confirm the efficacy of the trajectory optimization approach. For both processes, two different types of ground vehicle are used. Last, analytical comparisons with other techniques are also conducted for validation of the proposed framework.