Yevgen Chebotar | DeepAI

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Igor Mordatch
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∙ 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...

0 Yevgen Chebotar, et al. ∙

∙ 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...

0 Anthony Brohan, et al. ∙

∙ 05/05/2023

Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators

We describe a system for deep reinforcement learning of robotic manipula...

0 Alexander Herzog, et al. ∙

∙ 03/06/2023

PaLM-E: An Embodied Multimodal Language Model

Large language models excel at a wide range of complex tasks. However, e...

0 Danny Driess, et al. ∙

∙ 12/13/2022

RT-1: Robotics Transformer for Real-World Control at Scale

By transferring knowledge from large, diverse, task-agnostic datasets, m...

0 Anthony Brohan, et al. ∙

∙ 11/02/2022

Dual Generator Offline Reinforcement Learning

In offline RL, constraining the learned policy to remain close to the da...

0 Quan Vuong, et al. ∙

∙ 11/02/2022

Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints

Offline reinforcement learning (RL) learns policies entirely from static...

0 Anikait Singh, et al. ∙

∙ 07/12/2022

Inner Monologue: Embodied Reasoning through Planning with Language Models

Recent works have shown how the reasoning capabilities of Large Language...

8 Wenlong Huang, et al. ∙

∙ 04/04/2022

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Large language models can encode a wealth of semantic knowledge about th...

2 Michael Ahn, et al. ∙

∙ 02/03/2022

How to Leverage Unlabeled Data in Offline Reinforcement Learning

Offline reinforcement learning (RL) can learn control policies from stat...

24 Tianhe Yu, et al. ∙

∙ 11/09/2021

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale

Robotic skills can be learned via imitation learning (IL) using user-pro...

0 Yao Lu, et al. ∙

∙ 09/16/2021

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

Offline reinforcement learning (RL) algorithms have shown promising resu...

8 Tianhe Yu, et al. ∙

∙ 04/16/2021

MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale

General-purpose robotic systems must master a large repertoire of divers...

10 Dmitry Kalashnikov, et al. ∙

∙ 04/15/2021

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

We consider the problem of learning useful robotic skills from previousl...

11 Yevgen Chebotar, et al. ∙

∙ 03/26/2021

Visionary: Vision architecture discovery for robot learning

We propose a vision-based architecture search algorithm for robot manipu...

0 Iretiayo Akinola, et al. ∙

∙ 06/29/2020

Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

Robots need to be able to adapt to unexpected changes in the environment...

0 Giovanni Sutanto, et al. ∙

∙ 06/12/2019

Meta-Learning via Learned Loss

We present a meta-learning approach based on learning an adaptive, high-...

0 Yevgen Chebotar, et al. ∙

∙ 11/08/2018

Learning Latent Space Dynamics for Tactile Servoing

In order to achieve a dexterous robotic manipulation, we need to equip o...

0 Giovanni Sutanto, et al. ∙

∙ 10/12/2018

Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

We consider the problem of transferring policies to the real world by tr...

0 Yevgen Chebotar, et al. ∙

∙ 10/03/2016

Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search

In principle, reinforcement learning and policy search methods can enabl...

0 Ali Yahya, et al. ∙

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