Title: Cross-Embodied Co-Design for Dexterous Hands
Primary area: reinforcement learning
Keywords: Co-Design, Manipulation, Robotics, Cross Embodiment, Robot Hands, Robot Learning, Reinforcement Learning, Hardware Design

Abstract: Dexterous manipulation is limited by both control and design, with limited consensus as to which manipulators are best for performing dexterous tasks. This raises a fundamental challenge: how should we design robot manipulators that are optimized for dexterity? We present a co-design framework that jointly learns task-specific hand morphology and dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable deployment across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across 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.

TL;DR: A fast method for sim-to-real co-design of robot hands for dexterous manipulation through cross embodied policy training and physicially realistic grammars