What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?

no code implementations • 3 Oct 2023 • Sneha Silwal, Karmesh Yadav, Tingfan Wu, Jay Vakil, Arjun Majumdar, Sergio Arnaud, Claire Chen, Vincent-Pierre Berges, Dhruv Batra, Aravind Rajeswaran, Mrinal Kalakrishnan, Franziska Meier, Oleksandr Maksymets

We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.

Data Augmentation

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

no code implementations • NeurIPS 2023 • Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier

Contrary to inferences from prior work, we find that scaling dataset size and diversity does not improve performance universally (but does so on average).

Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation

no code implementations • 24 Mar 2022 • Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans, Lerrel Pinto

Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously explored in literature.

Imitation Learning