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.

翻译：本文提出了transition-net（转换网络），一种在真实世界环境中扩展机器人运动多样性的鲁棒转换策略。为此，我们首先将不同步态的复杂性分散到适用于真实机器人的专用运动策略中。随后，通过检查潜在状态表示，将这些策略通过鲁棒转换统一到单一连贯的元控制器中，从而扩展机器人的多样性。我们的方法使机器人能够迭代扩展其技能库，并在库中任意策略对之间实现鲁棒转换。在该框架下，新增技能不会改变先前习得的技能。此外，单张消费级GPU即可在不到一小时内完成运动策略的训练。实验表明，该方法在真实世界中有效，且相较于现有方法，在最具挑战性的转换对上平均成功率提高了19%。