Real-time synthesis of legged locomotion maneuvers in challenging industrial settings is still an open problem, requiring simultaneous determination of footsteps locations several steps ahead while generating whole-body motions close to the robot's limits. State estimation and perception errors impose the practical constraint of fast re-planning motions in a model predictive control (MPC) framework. We first observe that the computational limitation of perceptive locomotion pipelines lies in the combinatorics of contact surface selection. Re-planning contact locations on selected surfaces can be accomplished at MPC frequencies (50-100 Hz). Then, whole-body motion generation typically follows a reference trajectory for the robot base to facilitate convergence. We propose removing this constraint to robustly address unforeseen events such as contact slipping, by leveraging a state-of-the-art whole-body MPC (Croccodyl). Our contributions are integrated into a complete framework for perceptive locomotion, validated under diverse terrain conditions, and demonstrated in challenging trials that push the robot's actuation limits, as well as in the ICRA 2023 quadruped challenge simulation.

翻译：在复杂的工业环境中实现腿部运动技能的实时合成仍是一个未解决的难题，这要求在生成接近机器人极限的全身运动的同时，同步确定未来若干步的落脚点位置。状态估计与感知误差施加了实际约束，要求在模型预测控制（MPC）框架中快速重新规划运动。我们首先观察到，感知运动流水线的计算瓶颈在于接触面选择的组合优化问题。在选定接触面上重新规划接触位置可以在MPC频率（50-100 Hz）下完成。随后，全身运动生成通常需遵循机器人基座的参考轨迹以促进收敛。我们提出取消这一约束，通过采用最先进的全身MPC（Croccodyl）来稳健应对接触滑动等突发状况。我们的贡献被整合为一个完整的感知运动框架，并在多样化地形条件下得到验证，同时在推动机器人驱动极限的挑战性试验以及ICRA 2023四足机器人挑战赛模拟中进行了演示。