Safety-critical Motion Planning for Collaborative Legged Loco-Manipulation over Discrete Terrain

TL;DR

This paper presents a comprehensive motion planning framework for collaborative legged robots manipulating unknown payloads over discrete terrain, integrating global and decentralized MPCs with adaptive control, validated in simulation and hardware.

Contribution

It introduces a novel integrated approach combining global and decentralized MPCs with adaptive whole-body control for safe, obstacle-avoiding loco-manipulation on discrete terrain.

Findings

Successfully navigates obstacle courses with discrete terrain

Maintains stable manipulation of unknown payloads

Validated on Unitree robots in simulation and hardware

Abstract

As legged robots are deployed in industrial and autonomous construction tasks requiring collaborative manipulation, they must handle object manipulation while maintaining stable locomotion. The challenge intensifies in real-world environments, where they should traverse discrete terrain, avoid obstacles, and coordinate with other robots for safe loco-manipulation. This work addresses safe motion planning for collaborative manipulation of an unknown payload on discrete terrain while avoiding obstacles. Our approach uses two sets of model predictive controllers (MPCs) as motion planners: a global MPC generates a safe trajectory for the team with obstacle avoidance, while decentralized MPCs for each robot ensure safe footholds on discrete terrain as they follow the global trajectory. A model reference adaptive whole-body controller (MRA-WBC) then tracks the desired path, compensating for model uncertainties from the unknown payload. We validated our method in simulation and hardware on a team of Unitree robots. The results demonstrate that our approach successfully guides the team through obstacle courses, requiring planar positioning and height adjustments, and all happening on discrete terrain such as stepping stones.