RUKA: Rethinking the Design of Humanoid Hands with Learning

Anya Zorin, Irmak Guzey, Billy Yan, Aadhithya Iyer, Lisa Kondrich, Nikhil X. Bhattasali, Lerrel Pinto

Keywords: Mechanisms & Design, Grasping & Manipulation, Robot Learning

TL;DR: We designed a five fingered, tendon-driven, 3D-printable, compact, and repairable humanoid hand that leverages learned controllers.

Abstract:

Dexterous manipulation is a fundamental capability
for robotic systems, yet progress has been limited by hardware
trade-offs between precision, compactness, strength, and afford-
ability. 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. Tele-
operation tasks further showcase RUKA’s dexterous movements.
The open-source design and assembly instructions of RUKA, code,
and data are available at ruka-hand.github.io

Submission Number: 26