# RUKA: Rethinking the Design of Humanoid Hands with Learning

arXiv: https://arxiv.org/abs/2504.13165 · HTML: https://mlsanity.com/paper/2504.13165/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation](https://mlsanity.com/paper/2403.07870/) (2024)
- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation](https://mlsanity.com/paper/2309.00987/) (2023)
- [Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments](https://mlsanity.com/paper/2409.05865/) (2024)
- [Object-Centric Dexterous Manipulation from Human Motion Data](https://mlsanity.com/paper/2411.04005/) (2024)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards](https://mlsanity.com/paper/2410.23289/) (2024)
- [Lessons from Learning to Spin "Pens"](https://mlsanity.com/paper/2407.18902/) (2024)
- [RBO Hand 3 -- A Platform for Soft Dexterous Manipulation](https://mlsanity.com/paper/2201.10883/) (2022)
- [Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints](https://mlsanity.com/paper/2308.02453/) (2023)
- [Hierarchical Visual Policy Learning for Long-Horizon Robot Manipulation in Densely Cluttered Scenes](https://mlsanity.com/paper/2312.02697/) (2023)
- [Robust Anthropomorphic Robotic Manipulation through Biomimetic Distributed Compliance](https://mlsanity.com/paper/2404.05262/) (2024)

## Built on by (most cited first, 8 of 8)

- [Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations](https://mlsanity.com/paper/2511.16661/) (2025)
- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [House of Dextra: Cross-embodied Co-design for Dexterous Hands](https://mlsanity.com/paper/2512.03743/) (2025)
- [Human Universal Grasping](https://mlsanity.com/paper/2606.17054/) (2026)
- [Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridges](https://mlsanity.com/paper/2506.02489/) (2025)
- [Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning](https://mlsanity.com/paper/2603.26660/) (2026)
- [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)

Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordability. Existing control methods impose compromises on hand designs and applications. However, learning-based approaches present opportunities to rethink these trade-offs, particularly to address challenges with tendon-driven actuation and low-cost materials. This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers with 15 underactuated degrees of freedom enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form factor. To address control challenges, we learn joint-to-actuator and fingertip-to-actuator models from motion-capture data collected by the MANUS glove, leveraging the hand's morphological accuracy. Extensive evaluations demonstrate RUKA's superior reachability, durability, and strength compared to other robotic hands. Teleoperation tasks further showcase RUKA's dexterous movements. The open-source design and assembly instructions of RUKA, code, and data are available at https://ruka-hand.github.io/.