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[2602.17921] Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation
Summary
This article presents a novel co-design framework for optimizing end-effectors in robotics, specifically for manipulating deformable and fragile objects, enhancing both design and control strategies.
Why It Matters
The ability to manipulate delicate objects is crucial in various applications, from food handling to medical robotics. This research addresses existing limitations by integrating end-effector design and control, potentially improving efficiency and effectiveness in real-world scenarios.
Key Takeaways
- Introduces a co-design framework for end-effectors and control strategies.
- Utilizes latent diffeomorphic shape parameterization for design optimization.
- Implements a stress-aware bi-level optimization process.
- Demonstrates effectiveness through simulations and real-world food manipulation tasks.
- Provides a novel policy distillation scheme for zero-shot deployment.
Computer Science > Robotics arXiv:2602.17921 (cs) [Submitted on 20 Feb 2026] Title:Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation Authors:Kei Ikemura, Yifei Dong, Florian T. Pokorny View a PDF of the paper titled Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation, by Kei Ikemura and 2 other authors View PDF HTML (experimental) 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. Subjects: Robotics (cs.RO); Machine Learning (cs.LG...