用强化学习将粗略轨迹优化为符合真实物理约束的可行路径。
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
- 通过连续空间强化学习，显式引入二阶动力学约束来修正初始规划
- 能可靠恢复物理可行性，使规划轨迹满足速度与加速度限制
- 适合需要精确动力学执行的机器人任务，如复杂路径导航
在许多机器人任务中，智能体需遍历一系列空间区域以完成使命。这类问题本质上是离散-连续混合的：高层动作序列与物理可行的连续轨迹并存。生成的轨迹与动作序列还需满足时间窗、截止时间、速度或加速度限制等约束。现有混合时序规划器通常采用线性（一阶）动力学建模，无法保证结果满足机器人真实物理约束。因此，即使高层动作序列固定，生成动态可行轨迹仍是一个双层优化问题。本文提出在连续空间中使用强化学习解决该问题，定义一个显式包含解析二阶约束的马尔可夫决策过程，用于精炼由混合规划器生成的一阶轨迹。实验表明，该方法能可靠恢复物理可行性，有效弥合规划初始一阶轨迹与真实执行所需动力学之间的差距。
原文摘要 · Abstract (English)
In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequence and a physically feasible continuous trajectory. The resulting trajectory and action sequence must also satisfy problem constraints such as deadlines, time windows, and velocity or acceleration limits. While hybrid temporal planners attempt to address this challenge, they typically model motion using linear (first-order) dynamics, which cannot guarantee that the resulting plan respects the robot's true physical constraints. Consequently, even when the high-level action sequence is fixed, producing a dynamically feasible trajectory becomes a bi-level optimization problem. We address this problem via reinforcement learning in continuous space. We define a Markov Decision Process that explicitly incorporates analytical second-order constraints and use it to refine first-order plans generated by a hybrid planner. Our results show that this approach can reliably recover physical feasibility and effectively bridge the gap between a planner's initial first-order trajectory and the dynamics required for real execution.
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