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/.

The classification system for British track and field Para-athletes "could be abused" and is "open to exploitation", according to a UK Athletics review. The review found a "wide consensus" among those with experience of the system that rules could be exploited, also identifying methods of doing so. It follows claims before the Rio 2016 Paralympics that classifications could be manipulated to boost medal chances. Yet, there is "no substantive evidence" to suggest widespread cheating. A four-person panel, chaired by Paralympic wheelchair racer Anne Wafula Strike, conducted the review between November and February, with its findings revealed on Tuesday.