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

2026-02-20

AI Summary

This work addresses the challenges of manipulating deformable and fragile objects, which involve complex contact dynamics and stringent integrity requirements. The paper presents the first end-to-end co-design framework that jointly optimizes end-effector morphology and manipulation policy. The approach leverages implicit diffeomorphic shape parameterization, a stress-aware bilevel optimization pipeline, and a policy distillation mechanism that transfers knowledge from privileged simulation information to point cloud observations, enabling zero-shot deployment in the real world. Extensive experiments in both simulation and physical environments—spanning tasks such as jelly grasping/pushing and fish fillet scooping—demonstrate significant improvements in manipulation performance and generalization capability over existing methods.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Learning to Search
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• deformable object manipulation
• fragile object manipulation
• end-effector co-design
• contact dynamics
• object integrity

Innovation

• latent diffeomorphic parameterization
• co-design
• stress-aware optimization
• policy distillation
• deformable object manipulation