集成轨迹优化与强化学习的四足机器人跳跃及地形自适应落地方法
Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing
AI总结：
本文提出一种结合轨迹优化与强化学习的四足机器人安全落地框架，通过奖励松弛策略实现在粗糙地形上的自适应落地与精确跟踪，有效解决了非平坦地面的落地问题。
摘要：
跳跃是四足机器人运动能力的重要组成部分，包括动态起跳和自适应落地。现有的四足跳跃研究主要关注支撑期和飞行期，并假设落地区域为平坦地面，这在许多真实场景中并不现实。本文提出了一种安全落地框架，通过将轨迹优化（TO）与强化学习（RL）相结合，实现在粗糙地形上的自适应落地。RL智能体在粗糙地形环境中学习跟踪由TO生成的参考运动。为了在具有挑战性的地形上实现顺应性落地技能的学习，设计了一种奖励松弛策略，以鼓励在落地恢复期间的探索。大量实验验证了所提方法在多种场景中带来的精确跟踪和安全落地技能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
