通过可逆潜在分解学习凸集并集用于路径规划
Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning
AI总结：
提出ILD框架，联合学习可逆映射和潜在空间中的显式凸多面体并集，实现路径规划，并通过可见性引导采样保持凸集连通性，在多种环境中取得更高成功率。
摘要：
在杂乱的真实世界环境中进行无碰撞路径规划依赖于对无碰撞空间的表示，现有表示大致分为两类。显式表示（如凸集并集）可以作为硬的无碰撞约束嵌入基于优化的规划器中，但其参数随配置空间维度扩展性差。相比之下，隐式表示灵活且能很好地扩展到复杂几何形状，但通常缺乏此类保证。我们通过ILD（可逆潜在分解）弥合这一差距，该框架联合学习可逆映射和所得潜在空间中的显式凸多面体并集。规划在这些潜在凸集上进行，可逆映射将所得路径解码回原始配置空间，同时保持相对于细化后的显式安全区域的可行性。我们进一步提出可见性引导采样（VGS）以保持凸集连通性用于路径规划。在2D导航、6自由度（DoF）和14自由度操作环境中，ILD实现了比先前基线更广的覆盖、更好的集间连通性和更高的路径规划成功率，且在测试时细化后观察到零假阳性。在14自由度双臂操作器上，我们进一步展示了实时无碰撞规划，测试时细化适应了真实世界部署中单个6自由度臂的场景几何变化。
英文摘要：
Collision-free path planning in cluttered, real-world environments relies on a representation of the collision-free space, and existing representations broadly fall into two categories. Explicit representations, such as unions of convex sets, can be plugged into optimization-based planners as hard collision-free constraints, but their parameters scale poorly with configuration-space dimension. Implicit representations, by contrast, are flexible and scale well to complex geometries, yet typically lack such guarantees. We bridge this gap with ILD (Invertible Latent Decomposition), a framework that jointly learns an invertible mapping and a union of explicit convex polytopes in the resulting latent space. Planning is carried out over these latent convex sets, and the invertible mapping decodes the resulting paths back to the original configuration space while preserving feasibility with respect to the refined explicit safe regions. We further propose Visibility-Guided Sampling (VGS) to keep the convex sets connected for path planning. Across 2D navigation, 6-DoF, and 14-DoF manipulation environments, ILD achieves broader coverage, better inter-set connectivity, and higher path-planning success rates than prior baselines, with zero observed false positives after test-time refinement. On a 14-DoF bimanual manipulator, we further demonstrate real-time collision-free planning, with test-time refinement adapting to scene-geometry changes during real-world deployment on a single 6-DoF arm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
