通过逆动力学谱分解快速生成长时程机械臂操作轨迹
Spectral Decomposition of Inverse Dynamics for Fast Exploration in Model-Based Manipulation
- 利用逆动力学方程的谱分解生成正交且可行的轨迹分量
- 15秒内完成45秒、超过10种接触模式的复杂规划
- 适合需要长时间、多接触场景的机器人控制任务
长期机器人操作序列规划面临非线性接触动力学和多种接触模式带来的复杂性，且复杂度随规划时长增加而上升。本文提出一种基于逆动力学方程谱分解的搜索树方法，该方程将执行器位移映射到物体位移，其谱成分正交且近似可达集，同时保持动力学可行性。这些轨迹可与任意基于搜索的方法（如Rapidly-Exploring Random Trees, RRT）结合，用于长时程规划。实验表明，该方法在短时程任务中表现接近最新模型基规划方法，但关键优势在于解决长时程任务：现有方法失败时，本方法可在15秒内生成持续45秒、包含10+种接触模式的规划方案，展现出高度复杂场景下的实时能力。
原文摘要 · Abstract (English)
Planning long duration robotic manipulation sequences is challenging because of the complexity of exploring feasible trajectories through nonlinear contact dynamics and many contact modes. Moreover, this complexity grows with the problem's horizon length. We propose a search tree method that generates trajectories using the spectral decomposition of the inverse dynamics equation. This equation maps actuator displacement to object displacement, and its spectrum is efficient for exploration because its components are orthogonal and they approximate the reachable set of the object while remaining dynamically feasible. These trajectories can be combined with any search based method, such as Rapidly-Exploring Random Trees (RRT), for long-horizon planning. Our method performs similarly to recent work in model-based planning for short-horizon tasks, and differentiates itself with its ability to solve long-horizon tasks: whereas existing methods fail, ours can generate 45 second duration, 10+ contact mode plans using 15 seconds of computation, demonstrating real-time capability in highly complex domains.
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