b'Chelsea Finn'

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

∙ 09/11/2023

Robot Parkour Learning

Parkour is a grand challenge for legged locomotion that requires robots ...

0 Ziwen Zhuang, et al. ∙

research

∙ 08/24/2023

BridgeData V2: A Dataset for Robot Learning at Scale

We introduce BridgeData V2, a large and diverse dataset of robotic manip...

0 Homer Walke, 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

∙ 07/26/2023

Waypoint-Based Imitation Learning for Robotic Manipulation

While imitation learning methods have seen a resurgent interest for robo...

0 Lucy Xiaoyang Shi, et al. ∙

research

∙ 07/24/2023

Contrastive Example-Based Control

While many real-world problems that might benefit from reinforcement lea...

0 Kyle Hatch, et al. ∙

research

∙ 07/07/2023

Polybot: Training One Policy Across Robots While Embracing Variability

Reusing large datasets is crucial to scale vision-based robotic manipula...

0 Jonathan Yang, et al. ∙

research

∙ 07/07/2023

Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation

What makes generalization hard for imitation learning in visual robotic ...

0 Annie Xie, et al. ∙

research

∙ 06/26/2023

Supervised Pretraining Can Learn In-Context Reinforcement Learning

Large transformer models trained on diverse datasets have shown a remark...

0 Jonathan N. Lee, et al. ∙

research

∙ 06/19/2023

Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts

Effective machine learning models learn both robust features that direct...

0 Annie S. Chen, et al. ∙

research

∙ 06/14/2023

Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning

Whereas machine learning models typically learn language by directly tra...

0 Evan Zheran Liu, et al. ∙

research

∙ 06/08/2023

Conservative Prediction via Data-Driven Confidence Minimization

Errors of machine learning models are costly, especially in safety-criti...

0 Caroline Choi, et al. ∙

research

∙ 05/22/2023

Neural Functional Transformers

The recent success of neural networks as implicit representation of data...

0 Allan Zhou, et al. ∙

research

∙ 04/18/2023

Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets

Enabling robots to learn novel visuomotor skills in a data-efficient man...

4 Maximilian Du, et al. ∙

research

∙ 03/09/2023

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

A compelling use case of offline reinforcement learning (RL) is to obtai...

0 Mitsuhiko Nakamoto, et al. ∙

research

∙ 03/02/2023

Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning

In imitation and reinforcement learning, the cost of human supervision l...

0 Archit Sharma, 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

∙ 02/27/2023

Permutation Equivariant Neural Functionals

This work studies the design of neural networks that can process the wei...

0 Allan Zhou, et al. ∙

research

∙ 02/24/2023

Language-Driven Representation Learning for Robotics

Recent work in visual representation learning for robotics demonstrates ...

11 Siddharth Karamcheti, et al. ∙

research

∙ 02/10/2023

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features

Conventional approaches to robustness try to learn a model based on caus...

0 Annie S. Chen, et al. ∙

research

∙ 02/06/2023

Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts

Training machine learning models robust to distribution shifts is critic...

0 Amrith Setlur, et al. ∙

research

∙ 01/26/2023

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

The fluency and factual knowledge of large language models (LLMs) height...

0 Eric Mitchell, et al. ∙

research

∙ 01/19/2023

A Survey of Meta-Reinforcement Learning

While deep reinforcement learning (RL) has fueled multiple high-profile ...

0 Jacob Beck, et al. ∙

research

∙ 01/18/2023

NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis

Expert demonstrations are a rich source of supervision for training visu...

0 Allan Zhou, 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

∙ 12/08/2022

Learning Options via Compression

Identifying statistical regularities in solutions to some tasks in multi...

0 Yiding Jiang, et al. ∙

research

∙ 11/27/2022

Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models

A growing ecosystem of large, open-source foundation models has reduced ...

0 Eric Mitchell, et al. ∙

research

∙ 11/25/2022

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time

Distribution shift occurs when the test distribution differs from the tr...

0 Huaxiu Yao, et al. ∙

research

∙ 11/21/2022

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

While large pre-trained language models are powerful, their predictions ...

0 Eric Mitchell, et al. ∙

research

∙ 11/16/2022

Giving Feedback on Interactive Student Programs with Meta-Exploration

Developing interactive software, such as websites or games, is a particu...

0 Evan Zheran Liu, et al. ∙

research

∙ 10/25/2022

Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations

There is an inescapable long-tailed class-imbalance issue in many real-w...

0 Huaxiu Yao, et al. ∙

research

∙ 10/20/2022

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

A common approach to transfer learning under distribution shift is to fi...

6 Yoonho Lee, et al. ∙

research

∙ 10/19/2022

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

A long-term goal of reinforcement learning is to design agents that can ...

2 Annie Xie, et al. ∙

research

∙ 10/17/2022

You Only Live Once: Single-Life Reinforcement Learning

Reinforcement learning algorithms are typically designed to learn a perf...

3 Annie S. Chen, et al. ∙

research

∙ 10/11/2022

C-Mixup: Improving Generalization in Regression

Improving the generalization of deep networks is an important open chall...

7 Huaxiu Yao, et al. ∙

research

∙ 10/11/2022

Knowledge-Driven New Drug Recommendation

Drug recommendation assists doctors in prescribing personalized medicati...

24 Zhenbang Wu, et al. ∙

research

∙ 10/11/2022

Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials

Recent progress in deep learning highlights the tremendous potential of ...

21 Aviral Kumar, et al. ∙

research

∙ 10/06/2022

Learning to Reason With Relational Abstractions

Large language models have recently shown promising progress in mathemat...

6 Andrew J. Nam, 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

∙ 06/13/2022

Memory-Based Model Editing at Scale

Even the largest neural networks make errors, and once-correct predictio...

8 Eric Mitchell, et al. ∙

research

∙ 05/30/2022

Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning

Humans are capable of completing a range of challenging manipulation tas...

4 Maximilian Du, et al. ∙

research

∙ 05/11/2022

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

While reinforcement learning (RL) provides a framework for learning thro...

1 Archit Sharma, et al. ∙

research

∙ 04/18/2022

Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models

We present a data-efficient framework for solving sequential decision-ma...

12 Ali Ghadirzadeh, 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

∙ 03/15/2022

Vision-Based Manipulators Need to Also See from Their Hands

We study how the choice of visual perspective affects learning and gener...

1 Kyle Hsu, et al. ∙

research

∙ 03/03/2022

Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations

Spurious correlations pose a major challenge for robust machine learning...

3 Michael Zhang, et al. ∙

research

∙ 02/14/2022

Robust Policy Learning over Multiple Uncertainty Sets

Reinforcement learning (RL) agents need to be robust to variations in sa...

2 Annie Xie, et al. ∙

research

∙ 02/07/2022

Diversify and Disambiguate: Learning From Underspecified Data

Many datasets are underspecified, which means there are several equally ...

11 Yoonho Lee, et al. ∙

research

∙ 02/04/2022

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

In this paper, we study the problem of enabling a vision-based robotic m...

5 Eric Jang, 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. ∙

Chelsea Finn

Featured Co-authors