CaSCo: Cascade-Aware Soft-Collision Motion Planning

2026-09-16

AI Summary

为解决机器人运动中不同碰撞后果的问题，提出CaSCo框架，通过语义风险评估和物理模拟预测来规划最小化总风险的路径。

Abstract

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.

Problem

• collision
• semantic risk
• cascade-aware
• motion planning
• physical evolution

Innovation

• cascade-aware
• soft-collision
• semantic risk
• physics simulation
• graph-search algorithm