@inproceedings{
bhateja2023robotic,
title={Robotic Offline {RL} from Internet Videos via Value-Function Pre-Training},
author={Chethan Bhateja and Derek Guo and Dibya Ghosh and Anikait Singh and Manan Tomar and Quan Vuong and Yevgen Chebotar and Sergey Levine and Aviral Kumar},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=Rfc9zK6PNO}
}
Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinforcement learning (RL)? Offline RL methods, which learn from datasets of robot experience, offer one way to leverage prior data into the robotic learning pipeline. However, these methods have a "type mismatch" with video data (such as Ego4D), the largest prior datasets available for robotics, since video offers observation-only experience without the action or reward annotations needed for RL methods. In this paper, we develop a system for leveraging large-scale human video datasets in robotic offline RL, based entirely on learning value functions via temporal-difference learning. We show that value learning on video datasets learns representations that are more conducive to downstream robotic offline RL than other approaches for learning from video data. Our system, called V-PTR, combines the benefits of pre-training on video data with robotic offline RL approaches that train on diverse robot data, resulting in value functions and policies for manipulation tasks that perform better, act robustly, and generalize broadly. On several manipulation tasks on a real WidowX robot, our framework produces policies that greatly improve over prior methods. Our video and additional details can be found at https://dibyaghosh.com/vptr/index.html.
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Chethan Anand Bhateja Derek Guo Dibya Ghosh Anikait Singh Manan Tomar Quan Vuong Yevgen Chebotar Sergey Levine Aviral Kumar