Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion

TL;DR

This paper introduces a novel framework combining Monte-Carlo tree search and trajectory optimization to plan complex multi-contact locomotion for quadruped and humanoid robots, enabling dynamic and transferable motions in challenging environments.

Contribution

It presents the first integrated approach for simultaneous contact sequence and patch planning using whole-body dynamics for acyclic multi-contact locomotion.

Findings

Framework quickly finds diverse, dynamically consistent plans.

Plans are transferable to real quadruped robots.

Successfully applied to complex humanoid maneuvers.

Abstract

Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulation experiments, we show that our framework can quickly find a diverse set of dynamically consistent plans. We experimentally show that these plans are transferable to a real quadruped robot. We further show that the same framework can find highly complex acyclic humanoid maneuvers. To the best of our knowledge, this is the first demonstration of simultaneous contact sequence and patch selection for acyclic multi-contact locomotion using the whole-body dynamics of a quadruped.