  This work introduces DiffuseLoco, a framework for training multi-skill
diffusion-based policies for dynamic legged locomotion from offline datasets,
enabling real-time control of diverse skills on robots in the real world.
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 methods. To address this challenge, we propose 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 transfer to
real quadrupedal robots, and it can be deployed on edge computing devices.
Furthermore, DiffuseLoco demonstrates free transitions between skills and
robustness against environmental variations. Through extensive benchmarking in
real-world experiments, DiffuseLoco exhibits better stability and velocity
tracking performance compared to prior reinforcement learning and
non-diffusion-based behavior cloning baselines. The design choices are
validated via comprehensive ablation studies. This work opens new possibilities
for scaling up learning-based legged locomotion controllers through the scaling
of large, expressive models and diverse offline datasets.
