发表机构
Blavatnik School of Computer Science and Artificial Intelligence, Tel-Aviv University（特拉维夫大学布拉瓦尼克计算机科学与人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对刚体装配运动规划因几何约束面临的挑战，提出临界流形引导的CMG-RRT规划器。它利用配置空间分层细分，将采样偏向临界流形邻域引导探索，经证明概率完备，在基准测试中成功率达100%，还解决了麋鹿拆解谜题。
AI 中文摘要
由于严格的几何约束，刚体装配中的运动规划在机器人技术中构成了一项基本挑战。在这种情况下，可行的运动通常需要通过（接近）零间隙配置，其中部件通过接触受到严格约束。在这项工作中，我们引入了临界流形引导的快速扩展随机树（CMG-RRT），这是一种专门为紧密装配问题设计的基于采样的规划器。我们的关键观察结果是，在紧密装配中，有效的解决方案路径位于临界流形上或附近：配置空间的子集，由部件之间至少有一个接触点的姿态组成。CMG-RRT通过使用配置空间的分层细分，将采样自适应地偏向临界流形的邻域来引导探索。我们证明，在标准间隙假设下，CMG-RRT是概率完备的。在具有挑战性的旋转装配基准上的实证评估表明，在所有测试实例中成功率为100%，据我们所知，包括首次全自动解决麋鹿拆解谜题。我们的开源软件可通过我们的项目页面获取：此https URL。
英文摘要
Motion planning for rigid-body assembly poses a fundamental challenge in robotics due to tight geometric constraints. In such scenarios, feasible motions often require passing through (near-)zero clearance configurations in which the parts are tightly constrained by contact. In this work, we introduce Critical-Manifold Guided RRT (CMG-RRT), a sampling-based planner designed specifically for tight assembly problems. Our key observation is that in tight assemblies, valid solution paths lie on or near a critical manifold: the subset of configuration space consisting of poses with at least one contact point between parts. CMG-RRT guides exploration by adaptively biasing sampling toward neighborhoods of the critical manifold using a hierarchical subdivision of the configuration space. We prove that CMG-RRT is probabilistically complete under standard clearance assumptions. Empirical evaluation on challenging rotational assembly benchmarks demonstrates a 100% success rate across all tested instances, including, to the best of our knowledge, the first fully automatic solution of the Elk disentanglement puzzle. Our open source software is available through our project page: https://www.cgl.cs.tau.ac.il/projects/tight-assembly-planning.
CommentsAppeared in the 17th World Symposium on the Algorithmic Foundations of Robotics (WAFR)
