356 citations · 2.1k across the 32 of their papers we have counts for
34 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
Priya Sundaresan, Quan Vuong, Jiayuan Gu +10
Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over…
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid +3
Inspired by the success of transfer learning in computer vision, roboticists have investigated visual pre-training as a means to improve the learning efficiency and generalization…
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…
Video Language Planning
Yilun Du, Mengjiao Yang, Pete Florence +10
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pr…
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…
Contrastive Example-Based Control
Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov +4
While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and…
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama +9
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across l…
PaLM-E: An Embodied Multimodal Language Model
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi +19
Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding.…
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
Lantao Yu, Tianhe Yu, Jiaming Song +2
Offline imitation learning (IL) promises the ability to learn performant policies from pre-collected demonstrations without interactions with the environment. However, imitating be…
Scaling Robot Learning with Semantically Imagined Experience
Tianhe Yu, Ted Xiao, Austin Stone +10
Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios. One of the key contributing fa…
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal +48
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specif…
Offline Reinforcement Learning at Multiple Frequencies
Kaylee Burns, Tianhe Yu, Chelsea Finn +1
Leveraging many sources of offline robot data requires grappling with the heterogeneity of such data. In this paper, we focus on one particular aspect of heterogeneity: learning fr…
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Xi Chen, Ali Ghadirzadeh, Tianhe Yu +6
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting…
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar +3
Offline reinforcement learning (RL) can learn control policies from static datasets but, like standard RL methods, it requires reward annotations for every transition. In many case…
Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao +3
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model oft…
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar +3
Offline reinforcement learning (RL) algorithms have shown promising results in domains where abundant pre-collected data is available. However, prior methods focus on solving indiv…
Visual Adversarial Imitation Learning using Variational Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran +1
Reward function specification, which requires considerable human effort and iteration, remains a major impediment for learning behaviors through deep reinforcement learning. In con…
COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov +3
Model-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising parad…
Offline Reinforcement Learning from Images with Latent Space Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran +1
Offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. Offline RL enables extensive use and re-use of his…
Variable-Shot Adaptation for Online Meta-Learning
Tianhe Yu, Xinyang Geng, Chelsea Finn +1
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.…
Measuring and Harnessing Transference in Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao +3
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naive formulations often degrade performance and in…