Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work, we present a novel approach that optimizes gait sequences and respective timings for legged robots in the context of optimization-based controllers through the use of sampling-based methods and supervised learning techniques. We propose to bootstrap the search by learning an optimal value function in order to speed-up the gait planning procedure making it applicable in real-time. To validate our proposed method, we showcase its performance both in simulation and on hardware using a 22 kg electric quadruped robot. The method is assessed on different terrains, under external perturbations, and in comparison to a standard control approach where the gait sequence is fixed a priori.

翻译：足式机器人能够通过精心选择接触序列与时机，持续与环境交互，从而在复杂地形中实现导航。然而，接触规划背后的组合性质阻碍了此类优化问题在硬件上的实际应用。本研究提出一种新颖方法，通过采样方法与监督学习技术，在基于优化的控制器框架下，为足式机器人优化步态序列及其对应时序。我们提出通过学习最优价值函数来引导搜索过程，从而加速步态规划流程，使其能够适用于实时应用。为验证所提方法，我们使用一台22公斤重的电动四足机器人，在仿真与硬件实验中均展示了其性能。该方法在不同地形、外部扰动条件下进行了评估，并与采用固定先验步态序列的标准控制方法进行了对比。