5 citations · 12 across the 10 of their papers we have counts for
4 papers · 1 filter
Touch begins where vision ends: Generalizable policies for contact-rich manipulation
Zifan Zhao, Siddhant Haldar, Jinda Cui +2
Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generali…
eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
Venkatesh Pattabiraman, Zizhou Huang, Daniele Panozzo +3
If human experience is any guide, operating effectively in unstructured environments -- like homes and offices -- requires robots to sense the forces during physical interaction. Y…
EgoZero: Robot Learning from Smart Glasses
Vincent Liu, Ademi Adeniji, Haotian Zhan +4
Despite recent progress in general purpose robotics, robot policies still lag far behind basic human capabilities in the real world. Humans interact constantly with the physical wo…
Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu +5
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they st…