Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
Nico Bohlinger, Jonathan Kinzel, Daniel Palenicek, Lukasz Antczak, Jan Peters
Abstract
On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots. However, the computational constraints of real-time learning on robots pose a significant challenge. We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training utilizing the sample efficiency and minimal computational overhead of the new off-policy algorithm CrossQ. We investigate two control architectures: Predicting joint target positions for agile, high-speed locomotion and Central Pattern Generators for stable, natural gaits. While prior work focused on learning simple forward gaits, our framework extends on-robot learning to omnidirectional locomotion. We demonstrate the robustness of our approach in different indoor and outdoor environments and provide the videos and code for our experiments at: https://nico-bohlinger.github.io/gait_in_eight_website
BibTeX
@inproceedings{iros2025_gaitineighteffic,
title = {Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion},
author = {Nico Bohlinger and Jonathan Kinzel and Daniel Palenicek and Lukasz Antczak and Jan Peters},
booktitle = {IROS 2025},
year = {2025}
}