Kevin Zakka, Andy Zeng, Pete Florence, Jonathan Tompson, Jeannette Bohg, Debdidatta Dwibedi,
arXiv preprint 2021
project page / code
To leverage the vast quantity of tutorial videos on the web, we need robots that can learn from expert demonstrators with a vastly different embodiment. We tackle this cross-embodiment visual imitation problem by learning self-supervised reward functions that encode task progress and can be maximized with downstream reinforcement learning.
Kevin Zakka, Andy Zeng, Johnny Lee, Shuran Song,
ICRA 2020, ★ Best Paper Award in Automation Finalist ★
project page / blog post / arXiv / code / slides
We leverage visual geometric shape descriptors in the kit assembly task, with a nifty self-supervised data collection pipeline based on time-reversed disassembly, to create Form2Fit, a robotic system that can assemble novel objects and kits.