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.

本文提出了过渡网络（transition-net）, 它是一种强大的过渡策略，扩展了机器人在现实世界中的运动多样性。