A skill-based motion planning framework for humanoids

Abstract

This paper presents a multi-skill motion planner which is able to sequentially synchronize parameterized motion skills in order to achieve humanoid motions exhibiting complex whole-body coordination. The proposed approach integrates sampling-based motion planning in continuous parametric spaces with discrete search over skill choices, selecting the search strategy according to the functional type of each skill being coordinated. As a result, the planner is able to sequence arbitrary motion skills (such as reaching, balance adjustment, stepping, etc) in order to achieve complex motions needed for solving humanoid reaching tasks in realistic environments. The proposed framework is applied to the HOAP-3 humanoid robot and several results are presented.