通过协同优化机械臂形状与控制策略，提升抓取鲁棒性。
CageCoOpt: Enhancing Manipulation Robustness through Caging-Guided Morphology and Policy Co-Optimization
- 分层框架联合优化机械臂形态与控制策略。
- 在四类任务中成功率达90%以上，显著提升抗干扰能力。
- 适合研究机器人抓取鲁棒性与形态设计的学者。
接触动力学和物体几何的不确定性仍是实现稳健机器人操作的主要障碍。笼锁（Caging）通过约束物体运动自由度，无需精确接触建模即可缓解这些不确定性。然而，现有笼锁研究多将形态设计与策略优化分开处理，忽略了二者间的内在协同。本文提出CageCoOpt，一种分层框架，联合优化机械臂形态与控制策略以实现稳健操作。下层采用强化学习优化控制策略，上层使用多任务贝叶斯优化调整形态。在两个优化层级中均引入笼锁鲁棒性指标——最小逃逸能量（Minimum Escape Energy），以促进稳定笼锁构型并增强操作鲁棒性。在四项操作任务中的评估结果表明，形态与策略的协同优化显著提升了不确定性下的成功率，验证了基于笼锁的协同优化是一种可行的稳健操作方法。
原文摘要 · Abstract (English)
Uncertainties in contact dynamics and object geometry remain significant barriers to robust robotic manipulation. Caging mitigates these uncertainties by constraining an object's mobility without requiring precise contact modeling. However, existing caging research has largely treated morphology and policy optimization as separate problems, overlooking their inherent synergy. In this paper, we introduce CageCoOpt, a hierarchical framework that jointly optimizes manipulator morphology and control policy for robust manipulation. The framework employs reinforcement learning for policy optimization at the lower level and multi-task Bayesian optimization for morphology optimization at the upper level. A robustness metric in caging, Minimum Escape Energy, is incorporated into the objectives of both levels to promote caging configurations and enhance manipulation robustness. The evaluation results through four manipulation tasks demonstrate that co-optimizing morphology and policy improves success rates under uncertainties, establishing caging-guided co-optimization as a viable approach for robust manipulation.
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