用可微分度量优化物体重抓序列，提升规划稳定性与泛化能力。
Differentiable Object Pose Connectivity Metrics for Regrasp Sequence Optimization
- 通过能量模型构建连续可达性度量，支持梯度优化中间姿态。
- 在多个场景下实现最小步数重抓，成功率高于传统方法。
- 适合需跨夹爪、未知抓取点的机器人操作任务。
当单次抓取无法将物体从初始位姿转移到目标位姿且保持抓取可行性时，重抓规划成为必要。核心挑战在于对中间位姿间共享抓取连通性的推理，传统离散搜索易失效。本文提出一种基于可微分位姿序列连通性度量的隐式多步重抓规划框架。通过能量基模型（EBM）建模物体位姿下的抓取可行性，并利用能量可加性构建连续能量景观，以度量位姿对间的连通性，从而实现中间位姿的梯度优化。引入自适应迭代加深策略，自动确定最少中间步骤。实验表明，该成本函数提供平滑且信息丰富的梯度，显著提升规划鲁棒性。结果还显示模型具备对未见过的抓取位姿及跨末端执行器转移的泛化能力——在吸力约束下训练的模型可指导平行夹爪操作。多步规划结果进一步验证了自适应加深与最小步数搜索的有效性。
原文摘要 · Abstract (English)
Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibility. The main challenge is to reason about shared-grasp connectivity across intermediate poses, where discrete search becomes brittle. We propose an implicit multi-step regrasp planning framework based on differentiable pose sequence connectivity metrics. We model grasp feasibility under an object pose using an Energy-Based Model (EBM) and leverage energy additivity to construct a continuous energy landscape that measures pose-pair connectivity, enabling gradient-based optimization of intermediate object poses. An adaptive iterative deepening strategy is introduced to determine the minimum number of intermediate steps automatically. Experiments show that the proposed cost formulation provides smooth and informative gradients, improving planning robustness over other alternatives. They also demonstrate generalization to unseen grasp poses and cross-end-effector transfer, where a model trained with suction constraints can guide parallel gripper grasp manipulation. The multi-step planning results further highlight the effectiveness of adaptive deepening and minimum-step search.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。