Graph Inverse Reinforcement Learning from Diverse Videos

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.

reinforcement-learning Reinforcement Learning (RL) +1

Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation

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.

Reinforcement Learning (RL)

Self-Supervised Policy Adaptation during Deployment

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.

Dynamic Cloth Manipulation with Deep Reinforcement Learning

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