b'Karol Hausman'

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...

0 Yevgen Chebotar, et al. ∙

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...

0 Anthony Brohan, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 03/02/2023

Open-World Object Manipulation using Pre-trained Vision-Language Models

For robots to follow instructions from people, they must be able to conn...

0 Austin Stone, et al. ∙

research

∙ 03/01/2023

Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control

Recent progress in large language models (LLMs) has demonstrated the abi...

0 Wenlong Huang, et al. ∙

research

∙ 02/22/2023

Scaling Robot Learning with Semantically Imagined Experience

Recent advances in robot learning have shown promise in enabling robots ...

0 Tianhe Yu, et al. ∙

research

∙ 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. ∙

research

∙ 11/21/2022

Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

In recent years, much progress has been made in learning robotic manipul...

0 Ted Xiao, et al. ∙

research

∙ 09/16/2022

Code as Policies: Language Model Programs for Embodied Control

Large language models (LLMs) trained on code completion have been shown ...

0 Jacky Liang, et al. ∙

research

∙ 07/26/2022

Offline Reinforcement Learning at Multiple Frequencies

Leveraging many sources of offline robot data requires grappling with th...

13 Kaylee Burns, et al. ∙

research

∙ 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. ∙

research

∙ 04/05/2022

Jump-Start Reinforcement Learning

Reinforcement learning (RL) provides a theoretical framework for continu...

0 Ikechukwu Uchendu, et al. ∙

research

∙ 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. ∙

research

∙ 03/29/2022

Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning

Reinforcement learning systems have the potential to enable continuous i...

0 Abhishek Gupta, et al. ∙

research

∙ 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. ∙

research

∙ 12/17/2021

Autonomous Reinforcement Learning: Formalism and Benchmarking

Reinforcement learning (RL) provides a naturalistic framing for learning...

11 Archit Sharma, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 07/27/2021

Persistent Reinforcement Learning via Subgoal Curricula

Reinforcement learning (RL) promises to enable autonomous acquisition of...

2 Archit Sharma, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 11/14/2020

A Geometric Perspective on Self-Supervised Policy Adaptation

One of the most challenging aspects of real-world reinforcement learning...

18 Cristian Bodnar, et al. ∙

research

∙ 06/29/2020

Confidence-rich grid mapping

Representing the environment is a fundamental task in enabling robots to...

5 Ali-akbar Agha-mohammadi, et al. ∙

research

∙ 06/08/2020

Modeling Long-horizon Tasks as Sequential Interaction Landscapes

Complex object manipulation tasks often span over long sequences of oper...

0 Sören Pirk, et al. ∙

research

∙ 04/27/2020

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

Reinforcement learning provides a general framework for learning robotic...

0 Archit Sharma, et al. ∙

research

∙ 04/21/2020

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

One of the great promises of robot learning systems is that they will be...

29 Ryan Julian, et al. ∙

research

∙ 04/13/2020

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action fro...

17 Ted Xiao, et al. ∙

research

∙ 01/19/2020

Gradient Surgery for Multi-Task Learning

While deep learning and deep reinforcement learning (RL) systems have de...

19 Tianhe Yu, et al. ∙

research

∙ 10/25/2019

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

We present relay policy learning, a method for imitation and reinforceme...

14 Abhishek Gupta, et al. ∙

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 ...

45 Tianhe Yu, et al. ∙

research

∙ 10/01/2019

Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

The distributional perspective on reinforcement learning (RL) has given ...

1 Cristian Bodnar, et al. ∙

research

∙ 07/02/2019

Dynamics-Aware Unsupervised Discovery of Skills

Conventionally, model-based reinforcement learning (MBRL) aims to learn ...

0 Archit Sharma, et al. ∙

research

∙ 06/24/2019

Learning to Interactively Learn and Assist

When deploying autonomous agents in the real world, we need to think abo...

0 Mark Woodward, et al. ∙

research

∙ 06/24/2019

Training an Interactive Helper

Developing agents that can quickly adapt their behavior to new tasks rem...

1 Mark Woodward, et al. ∙

research

∙ 10/04/2018

Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations

Simulation-to-real transfer is an important strategy for making reinforc...

8 Zhanpeng He, et al. ∙

research

∙ 09/26/2018

Scaling simulation-to-real transfer by learning composable robot skills

We present a novel solution to the problem of simulation-to-real transfe...

10 Ryan Julian, et al. ∙

research

∙ 07/04/2018

Region Growing Curriculum Generation for Reinforcement Learning

Learning a policy capable of moving an agent between any two states in t...

0 Artem Molchanov, et al. ∙

Karol Hausman

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