# Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints

arXiv: https://arxiv.org/abs/2308.02453 · HTML: https://mlsanity.com/paper/2308.02453/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2023.

## Built on by (most cited first, 9 of 9)

- [GraspXL: Generating Grasping Motions for Diverse Objects at Scale](https://mlsanity.com/paper/2403.19649/) (2024)
- [Lessons from Learning to Spin "Pens"](https://mlsanity.com/paper/2407.18902/) (2024)
- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)
- [MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos](https://mlsanity.com/paper/2504.06084/) (2025)
- [RUKA: Rethinking the Design of Humanoid Hands with Learning](https://mlsanity.com/paper/2504.13165/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand](https://mlsanity.com/paper/2404.16483/) (2024)
- [CRAFT: A Tendon-Driven Hand with Hybrid Hard-Soft Compliance](https://mlsanity.com/paper/2603.12120/) (2026)
- [Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning](https://mlsanity.com/paper/2608.28578/) (2026)

## Abstract (arXiv)

Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand.