四足机器人首次实现连续跨越楼梯和碎石地形的敏捷跳跃
Agile Continuous Jumping in Discontinuous Terrains
- 分层控制框架：感知、规划、执行三层协同
- 单次跳跃跨两阶台阶，4.5秒完成3.5米高14阶楼梯
- 真实场景验证，通用策略适配多种障碍物跳跃任务
本文研究四足机器人在台阶、碎石等不连续地形上的敏捷、连续跳跃能力。与单步跳跃不同，连续跳跃需在长时程内精准执行高度动态动作，现有方法难以胜任。为此，我们设计了分层学习与控制框架：包含基于学习的高度图预测器以实现鲁棒地形感知，基于强化学习的质心级运动策略以支持多地形自适应规划，以及基于模型的低层腿部控制器以实现精确运动跟踪。同时，通过精确建模硬件特性最小化仿真到现实的差距。该框架使Unitree Go1机器人首次实现对人类尺寸台阶和稀疏踏石的敏捷连续跳跃。具体而言，机器人可单次跳跃跨越两阶台阶，在4.5秒内完成总长3.5米、高2.8米、共14阶的楼梯。此外，相同策略在多种障碍跳跃任务（如跨越水平或垂直断点）中均优于基线方法。
原文摘要 · Abstract (English)
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities. Experiment videos can be found at https://yxyang.github.io/jumping_cod/
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