790 citations · 2.7k across the 58 of their papers we have counts for
7 papers · 1 filter
Adaptation of Generalist Robot Policies with Minimal Data
Shreyas Kowshik, Sreyas Venkataraman, Leo Wang +3
A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning rema…
Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar +1
Large, general-purpose robotic policies trained on diverse demonstration datasets have been shown to be remarkably effective both for controlling a variety of robots in a range of…
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Yevgen Chebotar, Quan Vuong, Alex Irpan +22
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and auton…
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya +4
If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios m…
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Chethan Bhateja, Derek Guo, Dibya Ghosh +6
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 reinfo…
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Aviral Kumar, Anikait Singh, Frederik Ebert +4
Progress in deep learning highlights the tremendous potential of utilizing diverse robotic datasets for attaining effective generalization and makes it enticing to consider leverag…