Trinity Chung
32 posts
@Milotrince
@gs_ai_ technical staff; prev @CMU_Robotics @BerkeleyCDSS
1/ Progress in robot locomotion scaled with simulation, but touch was left behind. Our work, Tactile Genesis, unlocks sim for dexterous manipulation. Using sim to ablate tactile types and models, we can quickly converge to better tactile hardware and force-informed policies.
Presenting PTLD: an approach to learning tactile dexterous policies without ever simulating the tactile sensor. Tactile is essential for performing highly dexterous manipulation. However, collecting tactile observations reliably has been a fundamental bottleneck: (1) teleoperating a multi fingered hand for dynamic tasks is challenging, making sim-to-real imperative, (2) one can’t realistically simulate tactile today
It's clear that to unlock the next big advances in robotics, we need at-scale tactile sensing. For the past year, in collab w/ @gs_ai_, we've been working on perhaps the most wide-ranging, realistic tactile simulator to ask: *What should the future of robot hands look like?*
1/ Progress in robot locomotion scaled with simulation, but touch was left behind. Our work, Tactile Genesis, unlocks sim for dexterous manipulation. Using sim to ablate tactile types and models, we can quickly converge to better tactile hardware and force-informed policies.
1/ Progress in robot locomotion scaled with simulation, but touch was left behind. Our work, Tactile Genesis, unlocks sim for dexterous manipulation. Using sim to ablate tactile types and models, we can quickly converge to better tactile hardware and force-informed policies.
