Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots with comparable responsiveness therefore requires hardware that can retain and use these complementary sensory signals. Most imitation-learning systems, however, observe...
Conor W. Hayes, Rickmer Krohn, Aravind Ramaswami et al.· 0 citations
Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the vision of physical AI. While impressive dexterity has been demonstrated with simple grippers, multifingered hands offer the potential for substantially greater versatility, precision, and a...
Anthony Shilati, Anu Ramaswami, Luke C. Batteas et al.· 0 citations
It is demonstrated that fingertip deflection via the NURing enables fast, readily interpreted kinesthetic guidance that produces a coordinated whole-arm response without obstructing tactile contact, supporting its potential for eyes-free interaction and assistive applications.
Tomasz P Trzpit, Gregory Reardon, Anthony Shilati et al.· IEEE Transactions on Haptics· 0 citations
It is suggested that spatially distributed tactile feedback is essential for closing the gap between human and teleoperated dexterity and training the next generation of autonomous robots.
Rohan Kota, Gregory Reardon, J. Colgate· 0 citations
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