research
∙
09/18/2023
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for tr...
share
research
∙
07/28/2023
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
We study how vision-language models trained on Internet-scale data can b...
research
∙
07/24/2023
Contrastive Example-Based Control
While many real-world problems that might benefit from reinforcement lea...
research
∙
03/06/2023
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks. However, e...
research
∙
02/22/2023
Scaling Robot Learning with Semantically Imagined Experience
Recent advances in robot learning have shown promise in enabling robots ...
research
∙
12/13/2022
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, m...
research
∙
07/26/2022
Offline Reinforcement Learning at Multiple Frequencies
Leveraging many sources of offline robot data requires grappling with th...
research
∙
02/03/2022
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Offline reinforcement learning (RL) can learn control policies from stat...
research
∙
09/16/2021
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Offline reinforcement learning (RL) algorithms have shown promising resu...
research
∙
09/10/2021
Efficiently Identifying Task Groupings for Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
research
∙
07/16/2021
Visual Adversarial Imitation Learning using Variational Models
Reward function specification, which requires considerable human effort ...
research
∙
02/16/2021
COMBO: Conservative Offline Model-Based Policy Optimization
Model-based algorithms, which learn a dynamics model from logged experie...
research
∙
12/21/2020
Offline Reinforcement Learning from Images with Latent Space Models
Offline reinforcement learning (RL) refers to the problem of learning po...
research
∙
12/14/2020
Variable-Shot Adaptation for Online Meta-Learning
Few-shot meta-learning methods consider the problem of learning new task...
research
∙
10/29/2020
Measuring and Harnessing Transference in Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
research
∙
05/27/2020
MOPO: Model-based Offline Policy Optimization
Offline reinforcement learning (RL) refers to the problem of learning po...
research
∙
01/19/2020
Gradient Surgery for Multi-Task Learning
While deep learning and deep reinforcement learning (RL) systems have de...
research
∙
10/24/2019
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Meta-reinforcement learning algorithms can enable robots to acquire new ...
research
∙
09/20/2019
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Providing a suitable reward function to reinforcement learning can be di...
research
∙
02/14/2019
Unsupervised Visuomotor Control through Distributional Planning Networks
While reinforcement learning (RL) has the potential to enable robots to ...
research
∙
10/25/2018
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
We consider the problem of learning multi-stage vision-based tasks on a ...
research
∙
02/05/2018
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing o...
research
∙
09/14/2017
One-Shot Visual Imitation Learning via Meta-Learning
In order for a robot to be a generalist that can perform a wide range of...
research
∙
05/08/2017
Real-Time User-Guided Image Colorization with Learned Deep Priors
We propose a deep learning approach for user-guided image colorization. ...
research
∙
12/01/2016
