RUKA: Rethinking the Design of Humanoid Hands with Learning

2025-04-17

AI Summary

Existing tendon-driven anthropomorphic hands struggle to balance precision, compactness, structural strength, and cost-effectiveness, while conventional control approaches are hindered by underactuation and low-cost material constraints. This paper introduces RUKA—a human-scale, 5-fingered, 15-degree-of-freedom underactuated tendon-driven hand—featuring a 3D-printed skeletal structure, off-the-shelf motors, and a tendon transmission system. We propose a novel hardware-software co-optimization paradigm: morphologically accurate mechanical design is synergistically integrated with a learning-based joint/fingertip-to-actuator mapping model trained on motion-capture data from the MANUS glove. A fully open-source ROS control stack and real-time teleoperation framework accompany the hardware. Experiments demonstrate RUKA’s superior reachability, durability, and grasping force compared to state-of-the-art counterparts, and successful execution of diverse dexterous teleoperation tasks. All hardware designs, source code, and datasets are publicly released.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Learning with Manifolds

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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/.

Problem

• Design affordable, compact, strong humanoid robotic hands
• Address control challenges in tendon-driven actuation systems
• Enable dexterous manipulation with learning-based approaches

Innovation

• Tendon-driven humanoid hand with 15 DOF
• 3D-printed parts and off-the-shelf components
• Learning-based control from motion-capture data