356 citations · 2.1k across the 32 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu +5
Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of…
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta +3
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, da…
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu, Tianhe Yu, Chelsea Finn +1
Providing a suitable reward function to reinforcement learning can be difficult in many real world applications. While inverse reinforcement learning (IRL) holds promise for automa…
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current…
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
Tianhe Yu, Pieter Abbeel, Sergey Levine +1
We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtas…
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Tianhe Yu, Chelsea Finn, Annie Xie +4
Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning fr…
One-Shot Visual Imitation Learning via Meta-Learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang +2
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured e…
Generalizing Skills with Semi-Supervised Reinforcement Learning
Chelsea Finn, Tianhe Yu, Justin Fu +2
Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the v…