What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?
Sneha Silwal, Karmesh Yadav, Tingfan Wu, Jay Vakil, Arjun Majumdar, Sergio Arnaud, Claire Chen, Vincent-Pierre Berges
Abstract
We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks. Our study involves five different PVRs, each trained for five distinct manipulation or indoor navigation tasks. We performed this evaluation using three different robots and two different policy learning paradigms. From this e ort, we can arrive at three insights: 1) the performance trends of PVRs in the simulation are generally indicative of their trends in the real world, 2) the use of PVRs enables a first-of-its-kind result with indoor ImageNav (zero-shot transfer to a held-out scene in the real world), and 3) the benefits from variations in PVRs, primarily data-augmentation and fine-tuning, also transfer to the real-world performance. See project website1 for additional details and visuals.
BibTeX
@inproceedings{icra2024_whatdowelearnfro,
title = {What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?},
author = {Sneha Silwal and Karmesh Yadav and Tingfan Wu and Jay Vakil and Arjun Majumdar and Sergio Arnaud and Claire Chen and Vincent-Pierre Berges and Dhruv Batra and Aravind Rajeswaran and Mrinal Kalakrishnan and Franziska Meier and Oleksandr Maksymets},
booktitle = {ICRA 2024},
year = {2024}
}