This paper proposes the transition-net, a robust transition strategy that expands the versatility of robot locomotion in the real-world setting. To this end, we start by distributing the complexity of different gaits into dedicated locomotion policies applicable to real-world robots. Next, we expand the versatility of the robot by unifying the policies with robust transitions into a single coherent meta-controller by examining the latent state representations. Our approach enables the robot to iteratively expand its skill repertoire and robustly transition between any policy pair in a library. In our framework, adding new skills does not introduce any process that alters the previously learned skills. Moreover, training of a locomotion policy takes less than an hour with a single consumer GPU. Our approach is effective in the real-world and achieves a 19% higher average success rate for the most challenging transition pairs in our experiments compared to existing approaches.
@article{arxiv.2306.08224,
title = {Expanding Versatility of Agile Locomotion through Policy Transitions Using Latent State Representation},
author = {Guilherme Christmann and Ying-Sheng Luo and Jonathan Hans Soeseno and Wei-Chao Chen},
journal= {arXiv preprint arXiv:2306.08224},
year = {2023}
}