Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation

TL;DR

This paper introduces a minimally-guided, integrated planning framework for legged robots that automatically discovers complex contact-rich behaviors for diverse tasks, combining multiple planning techniques within a Task and Motion Planning approach.

Contribution

It presents a novel bilevel search strategy that integrates trajectory optimization, graph search, and sampling-based planning for holistic loco-manipulation in pre-modeled environments.

Findings

Emergent behaviors for quadrupedal mobile manipulators performing real-world tasks.

Automatic discovery of whole-body trajectories and contact schedules.

Successful deployment on real robotic systems.

Abstract

Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or pre-recorded expert demonstrations. Here, we propose a minimally-guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in pre-modeled environments. The key insight is that multi-modal problems of this nature can be formulated and treated within the context of integrated Task and Motion Planning (TAMP). An effective bilevel search strategy is achieved by incorporating domain-specific rules and adequately combining the strengths of different planning techniques: trajectory optimization and informed graph search coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and non-prehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors are also deployed on the real system using a two-layer whole-body tracking controller.