Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation

TL;DR

This work presents the first co-design framework that jointly optimizes end-effector morphology and manipulation control for deformable and fragile object manipulation, and introduces a latent diffeomorphic shape parameterization enabling expressive yet tractable end-effector geometry optimization.

Abstract

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.

Figures (17)

• Figure 1: We jointly optimize end-effector morphology and motion-adaptive control to enable safe, gentle manipulation of deformable and fragile objects. The co-designed end-effector reshapes contact geometry and force distribution, achieving reliable grasping while preserving object integrity, where baseline designs fail and cause breakage.
• Figure 2: Overview of the proposed co-design framework for deformable and fragile object manipulation. (a) A diffeomorphic design space $\mathcal{D}$ parameterizes physically valid end-effector geometries. (b) A derivative-free evolutionary optimizer (CMA-ES) searches $\mathcal{D}$ to identify an optimal design $d^*$ that maximizes task performance while minimizing contact-induced damage. (c) For each sampled design $d_i$, a design-conditioned controller $\pi^*_{d_i,\text{prv}}$ is optimized using simulator's privileged signals such as object stress. (d) Rollouts of the privileged policy on the optimized design $d$ are used to collect pointcloud observations and train a student diffusion policy $\pi^*_{d,\text{pcd}}$. (e) The distilled policy is deployed zero-shot on the real robot using pointcloud observations, preserving gentle manipulation behaviors learned in simulation.
• Figure 3: Deformation of a 2D gripper contour under a stationary velocity field (SVF). The upper region is frozen (zero velocity), while the lower region deforms smoothly.
• Figure 4: Examples of diffeomorphic deformation parameter fitting. The target shape surface (a) and the fitted mesh (b) are close in terms of Chamfer distance.
• Figure 5: End-effector design evolution for grasping a large jelly cube during a typical optimization run. High-scoring designs exhibit larger contact surfaces, which improve grasp stability and reduce contact-induced stress.