MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation

TL;DR

MetaLoco introduces a meta-reinforcement learning framework with motion imitation for universal quadrupedal locomotion, enabling zero-shot transfer across different robots without fine-tuning, demonstrated through extensive simulation and real-world tests.

Contribution

The paper presents a novel meta-RL approach with a memory-augmented policy that generalizes across diverse quadruped robots using minimal reference motions.

Findings

Achieves zero-shot locomotion transfer to unseen robots

Memory unit enhances generalization and adaptation

Effective in both simulation and real-world experiments

Abstract

This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms. The proposed method trains an RL agent equipped with a memory unit to imitate reference motions using a small set of procedurally generated quadruped robots. Through comprehensive simulation and real-world hardware experiments, we demonstrate the efficacy of our approach in achieving locomotion across various robots without requiring robot-specific fine-tuning. Furthermore, we highlight the critical role of the memory unit in enabling generalization, facilitating rapid adaptation to changes in the robot properties, and improving sample efficiency.