通过优先选择接触点，实现复杂场景中物体稳定放置的高效规划。
Stable Object Placement Planning From Contact Point Robustness
- 先选接触点再定姿态，避免传统采样评估的低效。
- 相比无启发式算法快20倍，比主流方法快8倍，成功率更高。
- 适用于任意形状、密度分布的刚体，适合实际机器人部署。
我们提出一种新型规划器，用于指导机器人在复杂场景中稳定放置物体。与传统方法不同，该方法先选定接触点，再确定能激发这些接触点的放置姿态，而非先采样姿态、再识别接触点并评估质量。该算法支持任意形状、非凸或密度不均的物体，且避免组合爆炸问题。基于物理原理设计的稳定性启发式使规划速度提升约20倍（相比无启发式版本），较当前最优方法快8倍。在10次真实机器人实验中验证，其稳定放置成功率优于5种基准算法。该方法具备通用性与可扩展性，适用于刚体物体的稳定放置规划。
原文摘要 · Abstract (English)
We introduce a planner designed to guide robot manipulators in stably placing objects within intricate scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. Our proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our planner offers a general and scalable method to tackle the problem of object placement planning with rigid objects.
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