What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?-Reference-Cited by-同舟云学术

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What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?

Published:2024-05-13 Issue: Volume: Page:17515-17521
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
language:
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Author:

Silwal Sneha¹,Yadav Karmesh²,Wu Tingfan¹,Vakil Jay¹,Majumdar Arjun²,Arnaud Sergio¹,Chen Claire³,Berges Vincent-Pierre¹,Batra Dhruv¹,Rajeswaran Aravind¹,Kalakrishnan Mrinal¹,Meier Franziska¹,Maksymets Oleksandr¹

Affiliation:

1. Meta AI

2. Georgia Institute of Technology

3. Stanford University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10610218.pdf?arnumber=10610218

Reference28 articles.

1. Simple but Effective: CLIP Embeddings for Embodied AI

2. The Unsurprising Effectiveness of Pre-Trained Vision Models for Control;Paгisi;ICML,2022

3. Offline visual representation learning for embodied navigation;Yadav,2022

4. R3M: A Universal Visual Representation for Robot Manipulation;Nair;CoRL,2022

5. Vip: Towards universal visual reward and representation via value-implicit pre-training;Ma,2022

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