Guiding Collision-Free Humanoid Multi-Contact Locomotion using Convex Kinematic Relaxations and Dynamic Optimization

TL;DR

This paper introduces a convex optimization-based framework for collision-free, multi-contact humanoid robot navigation that generates feasible motion plans quickly, even in complex environments with obstacles.

Contribution

It extends convex relaxation techniques to humanoid multi-contact planning, enabling rapid, feasible trajectory generation near kinematic and dynamic limits.

Findings

Plans generated within a few seconds

Successfully navigated complex environments in simulation

Produced dynamically feasible multi-contact trajectories

Abstract

Humanoid robots rely on multi-contact planners to navigate a diverse set of environments, including those that are unstructured and highly constrained. To synthesize stable multi-contact plans within a reasonable time frame, most planners assume statically stable motions or rely on reduced order models. However, these approaches can also render the problem infeasible in the presence of large obstacles or when operating near kinematic and dynamic limits. To that end, we propose a new multi-contact framework that leverages recent advancements in relaxing collision-free path planning into a convex optimization problem, extending it to be applicable to humanoid multi-contact navigation. Our approach generates near-feasible trajectories used as guides in a dynamic trajectory optimizer, altogether addressing the aforementioned limitations. We evaluate our computational approach showcasing three different-sized humanoid robots traversing a high-raised naval knee-knocker door using our proposed framework in simulation. Our approach can generate motion plans within a few seconds consisting of several multi-contact states, including dynamic feasibility in joint space.