no code implementations • 28 Jul 2022 • Sateesh Kumar, Jonathan Zamora, Nicklas Hansen, Rishabh Jangir, Xiaolong Wang
Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks.
no code implementations • 19 Jan 2022 • Rishabh Jangir, Nicklas Hansen, Sambaran Ghosal, Mohit Jain, Xiaolong Wang
We propose a setting for robotic manipulation in which the agent receives visual feedback from both a third-person camera and an egocentric camera mounted on the robot's wrist.
2 code implementations • ICLR 2021 • Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang
A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.
no code implementations • 31 Oct 2019 • Rishabh Jangir, Guillem Alenya, Carme Torras
Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i. e., using a reduced observation space, a few demonstrations, and a sparse reward.
Robotics