b'Sergey Levine'

research

∙ 08/16/2022

A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

Deep reinforcement learning is a promising approach to learning policies...

Laura Smith, et al. ∙

research

∙ 08/09/2022

Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience

This paper addresses the problem of inverse reinforcement learning (IRL)...

Marwa Abdulhai, et al. ∙

research

∙ 08/01/2022

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

We address the problem of enabling quadrupedal robots to perform precise...

Yandong Ji, 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...

Wenlong Huang, et al. ∙

research

∙ 07/11/2022

Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning

Reinforcement learning (RL) algorithms hold the promise of enabling auto...

Homer Walke, et al. ∙

research

∙ 07/10/2022

LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action

Goal-conditioned policies for robotic navigation can be trained on large...

Dhruv Shah, et al. ∙

research

∙ 07/05/2022

Offline RL Policies Should be Trained to be Adaptive

Offline RL algorithms must account for the fact that the dataset they ar...

Dibya Ghosh, et al. ∙

research

∙ 07/02/2022

Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation

Iterative refinement – start with a random guess, then iteratively impro...

Michael Chang, et al. ∙

research

∙ 06/21/2022

Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

Learned models and policies can generalize effectively when evaluated wi...

Katie Kang, et al. ∙

research

∙ 06/15/2022

Contrastive Learning as Goal-Conditioned Reinforcement Learning

In reinforcement learning (RL), it is easier to solve a task if given a ...

Benjamin Eysenbach, et al. ∙

research

∙ 06/07/2022

Imitating Past Successes can be Very Suboptimal

Prior work has proposed a simple strategy for reinforcement learning (RL...

Benjamin Eysenbach, et al. ∙

research

∙ 06/03/2022

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Supervised learning methods trained with maximum likelihood objectives o...

Amrith Setlur, et al. ∙

research

∙ 05/27/2022

Multimodal Masked Autoencoders Learn Transferable Representations

Building scalable models to learn from diverse, multimodal data remains ...

Xinyang Geng, et al. ∙

research

∙ 05/24/2022

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

How can we train an assistive human-machine interface (e.g., an electrom...

Siddharth Reddy, et al. ∙

research

∙ 05/20/2022

Planning with Diffusion for Flexible Behavior Synthesis

Model-based reinforcement learning methods often use learning only for t...

Michael Janner, et al. ∙

research

∙ 05/17/2022

Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space

General-purpose robots require diverse repertoires of behaviors to compl...

Kuan Fang, et al. ∙

research

∙ 05/04/2022

ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters

The incredible feats of athleticism demonstrated by humans are made poss...

Xue Bin Peng, et al. ∙

research

∙ 04/28/2022

Control-Aware Prediction Objectives for Autonomous Driving

Autonomous vehicle software is typically structured as a modular pipelin...

Rowan McAllister, et al. ∙

research

∙ 04/27/2022

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Building generalizable goal-conditioned agents from rich observations is...

Philippe Hansen-Estruch, et al. ∙

research

∙ 04/18/2022

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

Model-based reinforcement learning (RL) algorithms designed for handling...

Homanga Bharadhwaj, et al. ∙

research

∙ 04/18/2022

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Conventionally, generation of natural language for dialogue agents may b...

Siddharth Verma, et al. ∙

research

∙ 04/18/2022

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

Goal-oriented dialogue systems face a trade-off between fluent language ...

Charlie Snell, et al. ∙

research

∙ 04/12/2022

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

Offline reinforcement learning (RL) algorithms can acquire effective pol...

Aviral Kumar, et al. ∙

research

∙ 04/05/2022

Jump-Start Reinforcement Learning

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

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...

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...

Abhishek Gupta, et al. ∙

research

∙ 03/04/2022

X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback

We aim to help users communicate their intent to machines using flexible...

Jensen Gao, et al. ∙

research

∙ 02/23/2022

ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints

Robotic navigation has been approached as a problem of 3D reconstruction...

Dhruv Shah, et al. ∙

research

∙ 02/17/2022

Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization

Black-box model-based optimization (MBO) problems, where the goal is to ...

Brandon Trabucco, et al. ∙

research

∙ 02/05/2022

ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning

Building assistive interfaces for controlling robots through arbitrary, ...

Sean Chen, 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...

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...

Tianhe Yu, et al. ∙

research

∙ 02/01/2022

Fully Online Meta-Learning Without Task Boundaries

While deep networks can learn complex functions such as classifiers, det...

Jathushan Rajasegaran, et al. ∙

research

∙ 12/20/2021

RvS: What is Essential for Offline RL via Supervised Learning?

Recent work has shown that supervised learning alone, without temporal d...

Scott Emmons, et al. ∙

research

∙ 12/17/2021

Autonomous Reinforcement Learning: Formalism and Benchmarking

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

Archit Sharma, et al. ∙

research

∙ 12/09/2021

Extending the WILDS Benchmark for Unsupervised Adaptation

Machine learning systems deployed in the wild are often trained on a sou...

Shiori Sagawa, et al. ∙

research

∙ 12/09/2021

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

Despite overparameterization, deep networks trained via supervised learn...

Aviral Kumar, et al. ∙

research

∙ 12/08/2021

CoMPS: Continual Meta Policy Search

We develop a new continual meta-learning method to address challenges in...

Glen Berseth, et al. ∙

research

∙ 12/07/2021

Information is Power: Intrinsic Control via Information Capture

Humans and animals explore their environment and acquire useful skills e...

Nicholas Rhinehart, et al. ∙

research

∙ 11/22/2021

Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments

Geometric methods for solving open-world off-road navigation tasks, by l...

Nitish Dashora, 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...

Yao Lu, et al. ∙

research

∙ 11/04/2021

Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning

Reinforcement learning can train policies that effectively perform compl...

Dhruv Shah, et al. ∙

research

∙ 10/27/2021

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data

The aim in imitation learning is to learn effective policies by utilizin...

Mengjiao Yang, et al. ∙

research

∙ 10/24/2021

Understanding the World Through Action

The recent history of machine learning research has taught us that machi...

Sergey Levine, et al. ∙

research

∙ 10/22/2021

C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks

Goal-conditioned reinforcement learning (RL) can solve tasks in a wide r...

Tianjun Zhang, et al. ∙

research

∙ 10/20/2021

Data-Driven Offline Optimization For Architecting Hardware Accelerators

Industry has gradually moved towards application-specific hardware accel...

Aviral Kumar, et al. ∙

research

∙ 10/18/2021

MEMO: Test Time Robustness via Adaptation and Augmentation

While deep neural networks can attain good accuracy on in-distribution t...

Marvin Zhang, et al. ∙

research

∙ 10/12/2021

Offline Reinforcement Learning with Implicit Q-Learning

Offline reinforcement learning requires reconciling two conflicting aims...

Ilya Kostrikov, et al. ∙

research

∙ 10/11/2021

Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World

Legged robots are physically capable of traversing a wide range of chall...

Laura Smith, et al. ∙

research

∙ 10/08/2021

Offline Meta-Reinforcement Learning for Industrial Insertion

Reinforcement learning (RL) can in principle make it possible for robots...

Tony Z. Zhao, et al. ∙

Sergey Levine

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