Designing a humanoid locomotion controller is challenging and classically split up in sub-problems. Footstep planning is one of those, where the sequence of footsteps is defined. Even in simpler environments, finding a minimal sequence, or even a feasible sequence, yields a complex optimization problem. In the literature, this problem is usually addressed by search-based algorithms (e.g. variants of A*). However, such approaches are either computationally expensive or rely on hand-crafted tuning of several parameters. In this work, at first, we propose an efficient footstep planning method to navigate in local environments with obstacles, based on state-of-the art Deep Reinforcement Learning (DRL) techniques, with very low computational requirements for on-line inference. Our approach is heuristic-free and relies on a continuous set of actions to generate feasible footsteps. In contrast, other methods necessitate the selection of a relevant discrete set of actions. Second, we propose a forecasting method, allowing to quickly estimate the number of footsteps required to reach different candidates of local targets. This approach relies on inherent computations made by the actor-critic DRL architecture. We demonstrate the validity of our approach with simulation results, and by a deployment on a kid-size humanoid robot during the RoboCup 2023 competition.

设计人形机器人的运动控制器是具有挑战性的，其中步态规划是其中一个子问题；我们提出了一种基于深度强化学习技术的高效步态规划方法，来在具有障碍物的局部环境中进行导航，并且提出了一种预测方法来快速估计到达不同的局部目标所需的步数，通过仿真结果和在RoboCup 2023比赛中的实际部署验证了我们的方法。

FootstepNet：一种高效的演员-评论家方法，用于快速的在线双足步行脚步规划和预测