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

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.

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Cited by in corpus (6)

• A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
• Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
• Nature's All-in-One: Multitasking Robots Inspired by Dung Beetles
• Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations
• Task and Motion Planning for Humanoid Loco-manipulation
• Hybrid Dynamics Modeling and Trajectory Planning for a Cable-Trailer System with a Quadruped Robot