b'Igor Mordatch'

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

∙ 05/23/2023

Improving Factuality and Reasoning in Language Models through Multiagent Debate

Large language models (LLMs) have demonstrated remarkable capabilities i...

4 Yilun Du, et al. ∙

research

∙ 05/04/2023

Masked Trajectory Models for Prediction, Representation, and Control

We introduce Masked Trajectory Models (MTM) as a generic abstraction for...

0 Philipp Wu, et al. ∙

research

∙ 03/27/2023

Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning

Most successes in robotic manipulation have been restricted to single-ar...

0 Satoshi Kataoka, 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

∙ 03/01/2023

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

∙ 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

∙ 11/24/2022

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop ...

0 John P. Agapiou, et al. ∙

research

∙ 11/23/2022

Multi-Environment Pretraining Enables Transfer to Action Limited Datasets

Using massive datasets to train large-scale models has emerged as a domi...

0 David Venuto, et al. ∙

research

∙ 11/17/2022

VeLO: Training Versatile Learned Optimizers by Scaling Up

While deep learning models have replaced hand-designed features across m...

0 Luke Metz, et al. ∙

research

∙ 10/24/2022

FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners

Large language models (LLM) trained using the next-token-prediction obje...

0 Hao Liu, et al. ∙

research

∙ 10/21/2022

Implicit Offline Reinforcement Learning via Supervised Learning

Offline Reinforcement Learning (RL) via Supervised Learning is a simple ...

0 Alexandre Piché, et al. ∙

research

∙ 10/20/2022

Composing Ensembles of Pre-trained Models via Iterative Consensus

Large pre-trained models exhibit distinct and complementary capabilities...

1 Shuang Li, 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

∙ 06/30/2022

Learning Iterative Reasoning through Energy Minimization

Deep learning has excelled on complex pattern recognition tasks such as ...

9 Yilun Du, et al. ∙

research

∙ 05/30/2022

Multi-Game Decision Transformers

A longstanding goal of the field of AI is a strategy for compiling diver...

4 Kuang-Huei Lee, et al. ∙

research

∙ 03/15/2022

Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning

Most successes in robotic manipulation have been restricted to single-ar...

0 Satoshi Kataoka, et al. ∙

research

∙ 03/15/2022

Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning

Assembly of multi-part physical structures is both a valuable end produc...

0 Seyed Kamyar Seyed Ghasemipour, et al. ∙

research

∙ 02/03/2022

Pre-Trained Language Models for Interactive Decision-Making

Language model (LM) pre-training has proven useful for a wide variety of...

68 Shuang Li, et al. ∙

research

∙ 01/18/2022

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Can world knowledge learned by large language models (LLMs) be used to a...

0 Wenlong Huang, et al. ∙

research

∙ 11/04/2021

Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning

Dexterous manipulation of arbitrary objects, a fundamental daily task fo...

0 Wenlong Huang, et al. ∙

research

∙ 11/04/2021

Unsupervised Learning of Compositional Energy Concepts

Humans are able to rapidly understand scenes by utilizing concepts extra...

5 Yilun Du, et al. ∙

research

∙ 10/14/2021

The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combin...

0 Joseph Suarez, et al. ∙

research

∙ 09/01/2021

Implicit Behavioral Cloning

We find that across a wide range of robot policy learning scenarios, tre...

5 Pete Florence, et al. ∙

research

∙ 07/14/2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Existing evaluation suites for multi-agent reinforcement learning (MARL)...

9 Joel Z. Leibo, et al. ∙

research

∙ 06/24/2021

Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation

We present Brax, an open source library for rigid body simulation with a...

0 C. Daniel Freeman, et al. ∙

research

∙ 06/24/2021

Model-Based Reinforcement Learning via Latent-Space Collocation

The ability to plan into the future while utilizing only raw high-dimens...

9 Oleh Rybkin, et al. ∙

research

∙ 06/02/2021

Decision Transformer: Reinforcement Learning via Sequence Modeling

We present a framework that abstracts Reinforcement Learning (RL) as a s...

0 Lili Chen, et al. ∙

research

∙ 03/09/2021

Pretrained Transformers as Universal Computation Engines

We investigate the capability of a transformer pretrained on natural lan...

0 Kevin Lu, et al. ∙

research

∙ 12/07/2020

Reset-Free Lifelong Learning with Skill-Space Planning

The objective of lifelong reinforcement learning (RL) is to optimize age...

0 Kevin Lu, et al. ∙

research

∙ 12/02/2020

Improved Contrastive Divergence Training of Energy Based Models

We propose several different techniques to improve contrastive divergenc...

3 Yilun Du, et al. ∙

research

∙ 11/24/2020

Energy-Based Models for Continual Learning

We motivate Energy-Based Models (EBMs) as a promising model class for co...

1 Shuang Li, et al. ∙

research

∙ 11/03/2020

Rearrangement: A Challenge for Embodied AI

We describe a framework for research and evaluation in Embodied AI. Our ...

2 Dhruv Batra, et al. ∙

research

∙ 10/27/2020

γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

We introduce the γ-model, a predictive model of environment dynamics wit...

15 Michael Janner, et al. ∙

research

∙ 07/09/2020

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Reinforcement learning is typically concerned with learning control poli...

9 Wenlong Huang, et al. ∙

research

∙ 04/16/2020

A Game Theoretic Framework for Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has recently gained immense in...

38 Aravind Rajeswaran, et al. ∙

research

∙ 04/13/2020

Compositional Visual Generation and Inference with Energy Based Models

A vital aspect of human intelligence is the ability to compose increasin...

1 Yilun Du, et al. ∙

research

∙ 12/03/2019

Adaptive Online Planning for Continual Lifelong Learning

We study learning control in an online lifelong learning scenario, where...

31 Kevin Lu, et al. ∙

research

∙ 09/17/2019

Emergent Tool Use From Multi-Agent Autocurricula

Through multi-agent competition, the simple objective of hide-and-seek, ...

2 Bowen Baker, et al. ∙

research

∙ 09/15/2019

Model Based Planning with Energy Based Models

Model-based planning holds great promise for improving both sample effic...

5 Yilun Du, et al. ∙

research

∙ 03/20/2019

Implicit Generation and Generalization in Energy-Based Models

Energy based models (EBMs) are appealing due to their generality and sim...

6 Yilun Du, et al. ∙

research

∙ 03/02/2019

Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents

The emergence of complex life on Earth is often attributed to the arms r...

6 Joseph Suarez, et al. ∙

research

∙ 01/29/2019

Multi Agent Reinforcement Learning with Multi-Step Generative Models

The dynamics between agents and the environment are an important compone...

0 Orr Krupnik, et al. ∙

research

∙ 11/06/2018

Concept Learning with Energy-Based Models

Many hallmarks of human intelligence, such as generalizing from limited ...

0 Igor Mordatch, et al. ∙

research

∙ 11/05/2018

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

We propose a plan online and learn offline (POLO) framework for the sett...

4 Kendall Lowrey, et al. ∙

research

∙ 03/20/2018

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Policy gradient methods have enjoyed great success in deep reinforcement...

0 Cathy Wu, et al. ∙

research

∙ 11/02/2017

Interpretable and Pedagogical Examples

Teachers intentionally pick the most informative examples to show their ...

0 Smitha Milli, et al. ∙

research

∙ 10/10/2017

Emergent Complexity via Multi-Agent Competition

Reinforcement learning algorithms can train agents that solve problems i...

0 Trapit Bansal, et al. ∙

research

∙ 10/10/2017

Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

Ability to continuously learn and adapt from limited experience in nonst...

0 Maruan Al-Shedivat, et al. ∙

research

∙ 09/13/2017

Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learnin...

0 Jakob N. Foerster, et al. ∙

Igor Mordatch

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