CaSCo：级联感知的软碰撞运动规划
CaSCo: Cascade-Aware Soft-Collision Motion Planning
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对传统碰撞规划忽视物体接触后果差异的问题，提出CaSCo框架，利用视觉-语言模型分配语义风险，结合物理模拟预测级联碰撞，搜索最小化总风险的最优路径，并通过实验验证其有效性。
AI中文摘要：
传统运动规划将碰撞视为二元约束，尽管与不同物体的接触可能产生截然不同的后果。机器人可能安全地擦过纸板箱，而即使与玻璃、笔记本电脑或不稳定物体的轻微接触也可能是不可取的。此外，直接的机器人-物体碰撞可能移动被接触的物体，并触发二次物体-物体碰撞，使得运动的风险取决于场景的物理演化，而不仅仅取决于机器人的几何路径。我们提出CaSCo，一个级联感知的软碰撞运动规划框架，其中视觉-语言模型或语言模型为物体分配语义风险，物理模拟器预测候选机器人运动的后果。CaSCo搜索一条路径，该路径最小化由机器人直接或通过级联碰撞间接移动的独特物体的总语义风险。由于碰撞会改变环境，我们用预测的物体排列和已产生风险的物体集合来增强路线图状态。我们开发了一种最优图搜索算法，该算法具有可采纳且一致的级联松弛启发式，以及用于高效搜索的缓存和剪枝机制。在杂乱操作环境中的实验评估了语义风险、级联推理、规划效率和真实机器人操作。
英文摘要：
Conventional motion planning treats collision as a binary constraint, although contact with different objects can have drastically different consequences. A robot may safely brush against a cardboard box while even minor contact with a glass, laptop, or unstable object may be undesirable. Moreover, a direct robot--object collision can move the contacted object and trigger secondary object--object collisions, making the risk of a motion depend on the physical evolution of the scene rather than only on the robot's geometric path. We present CaSCo, a cascade-aware soft-collision motion planning framework in which a vision-language or language model assigns semantic risk to objects and a physics simulator predicts the consequences of candidate robot motions. CaSCo searches for a path that minimizes the total semantic risk of the unique objects displaced either directly by the robot or indirectly through cascaded collisions. Because collisions change the environment, we augment roadmap states with the predicted object arrangement and the set of objects whose risk has already been incurred. We develop an optimal graph-search algorithm with an admissible and consistent cascade-relaxed heuristic and caching and pruning mechanisms for efficient search. Experiments in cluttered manipulation environments evaluate semantic risk, cascade reasoning, planning efficiency, and real-robot operation.
