首次联合优化夹爪形状与控制策略，提升柔韧易碎物操作性能。
Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation
- 用隐空间微分形变参数化设计可表达且可控的夹爪形态。
- 双层优化框架同步优化夹爪结构与操控策略，减少物体应力。
- 通过特权到点云的策略蒸馏，实现零样本真实部署。
由于复杂的接触动力学和对物体完整性的严格要求，操控柔韧易碎物体仍是机器人领域的基本挑战。现有方法通常孤立优化夹爪设计或控制策略，限制了性能上限。本文提出首个联合优化夹爪形态与操作控制的共设计框架。引入（1）隐空间微分形变参数化，实现表达性强且计算可控的夹爪几何优化；（2）应力感知的双层共设计流程，耦合形态与控制优化；（3）从特权模型到点云的策略蒸馏方案，支持零样本真实世界部署。在果冻抓取、推移及鱼片舀取等复杂食物操作任务中进行评估，仿真与真实实验均验证了方法的有效性。
原文摘要 · Abstract (English)
Manipulating deformable and fragile objects remains a fundamental challenge in robotics due to complex contact dynamics and strict requirements on object integrity. Existing approaches typically optimize either end-effector design or control strategies in isolation, limiting achievable performance. In this work, we present the first co-design framework that jointly optimizes end-effector morphology and manipulation control for deformable and fragile object manipulation. We introduce (1) a latent diffeomorphic shape parameterization enabling expressive yet tractable end-effector geometry optimization, (2) a stress-aware bi-level co-design pipeline coupling morphology and control optimization, and (3) a privileged-to-pointcloud policy distillation scheme for zero-shot real-world deployment. We evaluate our approach on challenging food manipulation tasks, including grasping and pushing jelly and scooping fillets. Simulation and real-world experiments demonstrate the effectiveness of the proposed method.
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