2 citations · 2 across the 9 of their papers we have counts for
9 papers
SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation
Wei-Cheng Tseng, Gashon Hussein, Yuzhu Dong +9
Evaluating generalist robot manipulation policies in the real world is expensive, slow, and difficult to scale. Action-conditioned video world models offer a scalable alternative b…
Learning Process Rewards via Success Visitation Matching for Efficient RL
Raymond Tsao, Andrew Wagenmaker, Sergey Levine
In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task i…
VIMPO: Value-Implicit Policy Optimization for LLMs
Zhewei Kang, Aosong Feng, Sergey Levine +2
Reinforcement learning with verifiable rewards has become a central tool for improving the reasoning ability of large language models, but current methods face a trade-off between…
Reversal Q-Learning
Aditya Oberai, Seohong Park, Sergey Levine
Iterative generative modeling techniques, such as flow matching, provide powerful tools to model complex behaviors for effective offline reinforcement learning (RL). In this work,…
RL Token: Bootstrapping Online RL with Vision-Language-Action Models
Charles Xu, Jost Tobias Springenberg, Michael Equi +4
Vision-language-action (VLA) models can learn to perform diverse manipulation skills "out of the box," but achieving the precision and speed that real-world tasks demand requires f…
Visual Pre-Training on Unlabeled Images using Reinforcement Learning
Dibya Ghosh, Sergey Levine
In reinforcement learning (RL), value-based algorithms learn to associate each observation with the states and rewards that are likely to be reached from it. We observe that many s…
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data
Nitish Dashora, Dibya Ghosh, Sergey Levine
Online reinforcement learning (RL) with sparse rewards poses a challenge partly because of the lack of feedback on states leading to the goal. Furthermore, expert offline data with…
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
Video Occupancy Models
Manan Tomar, Philippe Hansen-Estruch, Philip Bachman +4
We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact lat…