Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulation experiments, we show that our framework can quickly find a diverse set of dynamically consistent plans. We experimentally show that these plans are transferable to a real quadruped robot. We further show that the same framework can find highly complex acyclic humanoid maneuvers. To the best of our knowledge, this is the first demonstration of simultaneous contact sequence and patch selection for acyclic multi-contact locomotion using the whole-body dynamics of a quadruped.
Dhédin et al. (Mon,) studied this question.