Our human hands are masterpieces of power and precision, capable of typing, hammering, or delicately using chopsticks. Yet most robots today still rely on simple two-finger grippers in controlled settings because dexterous hands are costly and difficult to deploy. To close this gap, I will introduce my LEAP Hands, high-performance, low-cost, and easy-to-assemble robotic hands that have become the most widely used platform for dexterous manipulation research. LEAP Hand V1 employs motor-in-joint actuation for simplicity, while V2 introduces a novel hybrid rigid–soft structure that delivers exceptional strength and durability. I will then show how large-scale human video/motion data and simulation techniques can teach human-like manipulation skills across diverse environments. By tightly integrating mechanical design and machine learning, my open-source robot hands achieve unprecedented levels of dexterity for a variety of everyday tasks.
Kenneth Shaw
Kenneth Shaw is a Ph.D. student in the Robotics Institute at Carnegie Mellon University advised by Prof. Deepak Pathak. He earned his Bachelor’s degree in Electrical and Computer Engineering from Georgia Tech and is a recipient of the NSF Graduate Research Fellowship. His research focuses on dexterous robot hands, combining mechanical design with learning from human data techniques. He created multiple low-cost, highly dexterous LEAP Hand platforms that are open-source and accessible to the community. His work has been recognized as a Best Oral Paper Award Finalist at IEEE Humanoids 2023, on the cover of the IJRR RSS special issue, and as a Best Paper Award Finalist at the Scaling Robot Learning Workshop. For further details, visit: https://www.kennyshaw.net
This event is open to NYU students, faculty, and staff.
📍 In-Person Location: 5 Metrotech, LC400 [NYU ID required]
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