用强化学习学出快速抓取轨迹，提升抓球稳定性。
Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects
- 在仿真中用强化学习生成成功抓取轨迹，构建低维轨迹流形。
- 运行时直接映射物体初始状态到参考轨迹，无需在线优化。
- 接触前用柔顺控制吸收冲击，适合高速动态抓取任务。
快速抓取飞行物体因反应时间短、撞击不确定性及运动动力学约束而困难。本文在仿真中利用强化学习收集成功抓取轨迹，并学习一个低维的运动动力学轨迹流形。运行时，根据估计的物体初始状态直接映射至参考抓取轨迹，无需在线非线性优化。在接近接触时采用柔顺控制，增强撞击吸收能力与抓取稳定性。
原文摘要 · Abstract (English)
Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement learning in simulation to collect successful catching trajectories and learn a low-dimensional kinodynamic trajectory manifold. At run time, the estimated object initial state is mapped directly to a reference catching trajectory without online nonlinear optimization. The trajectory is tracked with compliant control near contact for improved impact absorption and capture stability.
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