Abstract: Dexterous manipulation is a fundamental capability for robotic systems to interact with the physical world, yet progress in this area has been limited by hardware. An ideal robotic hand must balance precision, compactness, strength, and affordability—requirements that remain challenging to achieve simultaneously. Existing hand designs impose trade-offs based on available control methods and target applications. However, learning-based approaches present an opportunity to rethink some of these trade-offs, particularly to address challenges associated with tendon-driven actuation and low-cost materials. In this work, we present RUKA, a tendon-driven humanoid hand that is simple, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers (including an opposable thumb) and underactuated control of 18 degrees of freedom, enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form. To tackle tendon-driven control challenges, we learn joint-to-actuator and fingertip-to-actuator models using motion-capture data, leveraging the hand’s morphological accuracy. We extensively evaluate RUKA against commonly used robotic hands and demonstrate its superior reachability, durability, and strength. We further apply RUKA in bimanual teleoperation tasks and showcase that RUKA can be used to perform various dexterous tasks. By addressing important trade-offs in robotic hand design, we believe RUKA opens new possibilities for advancing manipulation research and expanding its accessibility. All code, data, design models, and assembly instructions are open-source and available at our website.