1 code implementation • 1 Jun 2023 • Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data.
1 code implementation • 2 Mar 2023 • Siddhant Haldar, Jyothish Pari, Anant Rai, Lerrel Pinto
Given a weak base-policy trained by offline imitation of demonstrations, FISH computes rewards that correspond to the "match" between the robot's behavior and the demonstrations.
1 code implementation • 2 Dec 2021 • Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, Lerrel Pinto
One reason such complexities arise is because standard visual imitation frameworks try to solve two coupled problems at once: learning a succinct but good representation from the diverse visual data, while simultaneously learning to associate the demonstrated actions with such representations.
no code implementations • 19 Jul 2021 • Sarah Young, Jyothish Pari, Pieter Abbeel, Lerrel Pinto
In this work, we propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks.