AI 中文总结
本文提出 PEEL 算法，将尺度不变采样方案集成到 MAB-RRT 规划器中，并行规划长 horizon 多部件拆卸序列，在 76 个装配体上实现单部件拆卸 100% 成功率，并成功解决 Fetch 机械臂的长 horizon 拆卸问题。
AI 中文摘要
长 horizon 多部件物体拆卸要求机器人计算无碰撞移除运动的可行序列，即便存在狭窄的逃生通道。为高效解决这类拆卸问题，我们提出长 horizon 拆卸并行提取算法（PEEL），该算法可高效计算物体装配体的拆卸运动并将其输送给机械臂执行。PEEL 采用基于采样的运动规划，通过尺度不变采样方案计算单物体运动：在预热阶段估计物体尺度，后续的定向采样器利用该尺度信息。该采样方案被集成到多臂老虎机快速探索随机树（MAB-RRT）规划器中，规划器根据接收的奖励信号在不同采样器间切换。借助 MAB-RRT，PEEL 算法并行运行一批规划器，以获得指定部件移除顺序的有序图。我们展示，MAB-RRT 在 76 个装配体上的单部件拆卸任务中可实现 100% 成功率，且对其参数具有鲁棒性。通过将 MAB-RRT 集成到 PEEL 中，我们使用 Fetch 机械臂机器人解决了四个包含 10 至 17 个独立物体部件的长 horizon 拆卸问题。
英文摘要
Long-horizon multi-part object disassembly requires robots to compute feasible sequences of collision-free removal motions, even in the presence of tight, narrow escape corridors. To efficiently solve such disassembly problems, we propose Parallel Extraction for Long-Horizon Disassembly (PEEL), an algorithm which efficiently computes disassembly motions for object assemblies and feeds them to a robot manipulator for execution. PEEL uses sampling-based motion planning to compute single-object motions through the use of a scale-invariant sampling scheme, where the object scale is estimated in a burn-in phase and a subsequent directional sampler exploits the scale. This sampling scheme is integrated into a multi-arm bandit rapidly-exploring random tree (MAB-RRT) planner, which switches between different samplers depending on the reward signal received. Using MAB-RRT, the PEEL algorithm runs a batch of planners in parallel to obtain an ordered graph specifying the sequence in which object parts have to be removed. We show that MAB-RRT can efficiently solve single-part disassemblies with 100 percent success rate on 76 assemblies, and that it is robust to its parameters. By integrating MAB-RRT into PEEL, we solve four long-horizon disassembly problems using the Fetch manipulator robot involving 10 to 17 individual object parts.
Comments15 pages, 11 figures, 11 tables. Project page: https://peel-disassembly.surge.sh/
