A Unified Framework to Learn Collision-free Loco-Manipulation via Adversarial Motion Priors

Abstract

Designing a whole-body controller for loco-manipulation in unstructured real-world environments remains a formidable challenge. Previous approaches have primarily focused on extending the workspace of robotic arms while maintaining quadrupedal landing postures. However, these methods fail to fully exploit the mobility of legged robots. To address these limitations, we propose a unified framework for collision-free loco-manipulation in real-world applications. The framework comprises two key modules: (1) a Loco-manipulation Motion Prior, which generates loco-manipulation skill trajectories via Trajectory Optimization (TO), and (2) a Collision-free Manipulation module using a Model Predictive Path Integral (MPPI)-based trajectory generator and a vector-based trajectory follower. Extensive experiments have been conducted in both simulation and real-world scenarios to evaluate our framework's tracking accuracy, whole-body coordination, and workspace expansion capabilities.

Method

Main Results

Simulation

  High platform        On the ground

Real-World Deployment

  High platform        On the ground

Trajectory Tracking

Domain Randomization Parameters

Network Architecture

PPO Hyper-parameters