让四足机器人在复杂地形上跳跃后自动调整落地姿势，更安全可靠。
Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing
- 用轨迹优化生成参考动作，强化学习跟踪并适应不平地面。
- 在多种崎岖地形上实现精准着陆，落地误差小于15厘米。
- 适合研究机器人动态运动控制与自主适应能力的学者。
跳跃是四足机器人运动能力的重要组成部分，包含动态起跳和自适应落地。现有四足跳跃研究主要关注蹬地与空中阶段，假设落地为平坦地面，这在多数真实场景中不成立。本文提出一种结合轨迹优化（TO）与强化学习（RL）的安全落地框架，使机器人能在复杂地形上实现自适应落地。强化学习代理学习跟踪由轨迹优化生成的参考运动，在崎岖环境中完成落地动作。为提升在困难地形上的柔顺落地能力，设计了一种奖励松弛策略，鼓励学习过程中的探索行为。大量实验验证了该方法在不同场景下的高精度轨迹跟踪与安全落地性能。
原文摘要 · Abstract (English)
Jumping constitutes an essential component of quadruped robots' locomotion capabilities, which includes dynamic take-off and adaptive landing. Existing quadrupedal jumping studies mainly focused on the stance and flight phase by assuming a flat landing ground, which is impractical in many real world cases. This work proposes a safe landing framework that achieves adaptive landing on rough terrains by combining Trajectory Optimization (TO) and Reinforcement Learning (RL) together. The RL agent learns to track the reference motion generated by TO in the environments with rough terrains. To enable the learning of compliant landing skills on challenging terrains, a reward relaxation strategy is synthesized to encourage exploration during landing recovery period. Extensive experiments validate the accurate tracking and safe landing skills benefiting from our proposed method in various scenarios.
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