This study focuses on a layered, experience-based, multi-modal contact planning framework for agile quadrupedal locomotion over a constrained rebar environment. To this end, our hierarchical planner incorporates locomotion-specific modules into the high-level contact sequence planner and solves kinodynamically-aware trajectory optimization as the low-level motion planner. Through quantitative analysis of the experience accumulation process and experimental validation of the kinodynamic feasibility of the generated locomotion trajectories, we demonstrate that the experience planning heuristic offers an effective way of providing candidate footholds for a legged contact planner. Additionally, we introduce a guiding torso path heuristic at the global planning level to enhance the navigation success rate in the presence of environmental obstacles. Our results indicate that the torso-path guided experience accumulation requires significantly fewer offline trials to successfully reach the goal compared to regular experience accumulation. Finally, our planning framework is validated in both dynamics simulations and real hardware implementations on a quadrupedal robot provided by Skymul Inc.

翻译：本研究聚焦于一种分层式、基于经验的多模态接触规划框架，用于实现四足机器人在受限钢筋环境中的敏捷运动。为此，我们的分层规划器将运动专用模块集成到高层接触序列规划器，并将动力学感知轨迹优化作为低层运动规划器。通过经验积累过程的定量分析以及生成运动轨迹的动力学可行性实验验证，我们证明了经验规划启发式方法为腿部接触规划器提供候选落脚点是一种有效途径。此外，我们在全局规划层引入了躯干路径引导启发式方法，以提升存在环境障碍物时的导航成功率。结果表明，与常规经验积累相比，采用躯干路径引导的经验积累需要显著更少的离线试验即可成功抵达目标。最后，我们的规划框架在动力学仿真及Skymul Inc.提供的四足机器人真实硬件实现中均得到了验证。