DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets

Abstract

Offline learning at scale has led to breakthroughs in computer vision, natural language processing, and robotic manipulation domains. However, scaling up learning for legged robot locomotion, especially with multiple skills in a single policy, presents significant challenges for prior online reinforcement learning (RL) methods. To address this challenge, we propose DiffuseLoco, a novel, scalable framework that leverages diffusion models to directly learn from offline multimodal datasets with a diverse set of locomotion skills. With design choices tailored for real-time control in dynamical systems, including receding horizon control and delayed inputs, DiffuseLoco is capable of reproducing multimodality in performing various locomotion skills, zero-shot transferred to real quadruped robots and deployed on edge computes. Through extensive real-world benchmarking, DiffuseLoco exhibits better stability and velocity tracking performance compared to prior RL and non-diffusion-based behavior cloning baselines. This work opens new possibilities for scaling up learning-based legged locomotion control through the scaling of large, expressive models and diverse offline datasets.