Cross-embodied Co-design for Dexterous Hands

2025-12-03

AI Summary

Current dexterous hand design and control are largely decoupled, lacking unified evaluation metrics and co-optimization frameworks. Method: We propose the first end-to-end co-design framework that jointly optimizes hand morphology—fully parameterized across joints, fingers, and palm—and morphology-conditioned reinforcement learning control policies. Our approach integrates differentiable dynamics modeling, modular structural generative networks, and an automated simulation-to-reality transfer pipeline compatible with off-the-shelf mechanical components. Contribution/Results: We introduce a novel cross-morphology control evaluation mechanism, substantially enhancing design-space scalability. Empirical validation on in-hand rotation tasks demonstrates that the framework completes custom dexterous hand design, policy training, and physical deployment within 24 hours—achieving an efficient, closed-loop workflow from parametric search to real-world embodiment.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework will be open-sourced and available on our website.

Problem

• Co-designs hand morphology and control for dexterous tasks
• Enables scalable evaluation across diverse hand designs
• Facilitates rapid real-world fabrication and deployment

Innovation

• Co-design framework for hand morphology and control policies
• Morphology-conditioned cross-embodied control for scalable evaluation
• Real-world fabrication with accessible components in 24 hours