Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

2026-08-28

AI Summary

为了解决肌腱驱动手在仿真中难以学习的问题，本文提出了Aero Hand Open，并提供了仿真模型、驱动映射和强化学习训练包，以实现完全基于仿真的策略训练。

Abstract

Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effects produce that saving. Routing force through a cable removes the requirement that a motor fit inside the joint it drives, so smaller and cheaper motors suffice, and one motor can drive several joints through a single cable, so fewer motors are needed. They are also harder to learn on than a direct-drive hand. The underactuated transmission that produces the saving is itself difficult to represent in a simulator, and the joints one cable drives are not independently commandable. We present Aero Hand Open, a tendon-driven anthropomorphic hand that is released simulation-ready. Three things ship with it. A simulation model reproduces the cable transmission itself. An identified actuation map connects that model to the motor commands in both directions, including the three-way coupling of the thumb. A reinforcement learning package trains policies for the hand. Together they let a policy be trained entirely in simulation and run on the hand with no fine-tuning and no state estimation. We release the mechanical design, the simulation model, the identified mapping, the training environment and the deployment stack.

Problem

• tendon-driven hand
• dexterous manipulation
• simulation
• actuation map
• reinforcement learning

Innovation

• tendon-driven hand
• simulation model
• actuation map
• reinforcement learning