273 citations · 633 across the 38 of their papers we have counts for
7 papers · 1 filter
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam, Juyong Lee, Jesse Zhang +3
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In o…
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Jesse Zhang, Jiahui Zhang, Karl Pertsch +5
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior w…
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…
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold +5
Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL)…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Anthony Brohan, Noah Brown, Justice Carbajal +51
We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic…
SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
Jesse Zhang, Karl Pertsch, Jiahui Zhang +1
Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks. Prior works have defined pre-training tasks via natural languag…