S. Levine

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High-Dimensional Continuous Control Using Generalized Advantage Estimation pdf

Sergey Levine, Michael I. Jordan, Pieter Abbeel, 2015, ICLR.

Sergey Levine, Michael I. Jordan, Pieter Abbeel, 2015, ICML.

Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics

Sergey Levine, Pieter Abbeel, S. Levine, 2014, NIPS.

Learning dexterous manipulation for a soft robotic hand from human demonstrations

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

Sergey Levine, Peter Pastor, Alex Krizhevsky, 2016, Int. J. Robotics Res..

Reinforcement Learning with Deep Energy-Based Policies

Sergey Levine, Pieter Abbeel, Haoran Tang, 2017, ICML.

Dynamics-Aware Unsupervised Discovery of Skills pdf

Sergey Levine, Karol Hausman, Vikash Kumar, 2019, ICLR.

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control pdf

Sergey Levine, Chelsea Finn, Alex X. Lee, 2018, ArXiv.

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

Sergey Levine, Pieter Abbeel, Matthew J. Johnson, 2018, ICML.

Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders pdf

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2015, ArXiv.

Learning Flexible and Reusable Locomotion Primitives for a Microrobot

Sergey Levine, Kristofer S. J. Pister, Grant Wang, 2018, IEEE Robotics and Automation Letters.

Guiding Policies with Language via Meta-Learning pdf

John DeNero, Sergey Levine, Pieter Abbeel, 2018, ICLR.

Uncertainty-Aware Reinforcement Learning for Collision Avoidance pdf

Sergey Levine, Pieter Abbeel, Gregory Kahn, 2017, ArXiv.

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Sergey Levine, Pieter Abbeel, Gregory Kahn, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Learning to Walk in the Real World with Minimal Human Effort pdf

Sergey Levine, Sehoon Ha, Peng Xu, 2020, CoRL.

Robust Imitative Planning: Planning from Demonstrations Under Uncertainty

S. Levine, Rowan McAllister, P. Tigas, 2019 .

Wasserstein Dependency Measure for Representation Learning

Sergey Levine, Yoshua Bengio, Pierre Sermanet, 2019, NeurIPS.

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning pdf

Sergey Levine, Aviral Kumar, Xue Bin Peng, 2019, ArXiv.

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning pdf

Sergey Levine, Shixiang Gu, Julian Ibarz, 2017, ICLR.

Learning force-based manipulation of deformable objects from multiple demonstrations

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

End-to-End Learning of Semantic Grasping

Sergey Levine, Julian Ibarz, Sudheendra Vijayanarasimhan, 2017, CoRL.

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2018, ICML.

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks

Sergey Levine, Raia Hadsell, Mrinal Kalakrishnan, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Continuous Deep Q-Learning with Model-based Acceleration pdf

Sergey Levine, Ilya Sutskever, Shixiang Gu, 2016, ICML.

InfoBot: Transfer and Exploration via the Information Bottleneck pdf

Sergey Levine, Yoshua Bengio, Hugo Larochelle, 2019, ICLR.

Time-Contrastive Networks: Self-Supervised Learning from Video

Sergey Levine, Stefan Schaal, Yevgen Chebotar, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Time-Contrastive Networks : Self-Supervised Learning from Pixels

S. Levine, Pierre Sermanet, Eric Jang, 2017 .

Recurrent Network Models for Kinematic Tracking pdf

Jitendra Malik, Sergey Levine, Katerina Fragkiadaki, 2015, ArXiv.

Physically plausible simulation for character animation

Sergey Levine, Jovan Popovic, S. Levine, 2012, SCA '12.

Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching

Sergey Levine, Roberto Calandra, Justin Lin, 2019, 2019 International Conference on Robotics and Automation (ICRA).

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations pdf

Sergey Levine, Aravind Rajeswaran, Emanuel Todorov, 2017, Robotics: Science and Systems.

Data-efficient Learning of Morphology and Controller for a Microrobot

Sergey Levine, Grant Wang, Brian Yang, 2019, 2019 International Conference on Robotics and Automation (ICRA).

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards pdf

Sergey Levine, Eugen Solowjow, Jianlan Luo, 2019, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Gesture controllers

Sergey Levine, Sebastian Thrun, Vladlen Koltun, 2010, SIGGRAPH 2010.

Policy Learning with Continuous Memory States for Partially Observed Robotic Control pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2015, ArXiv.

EMI: Exploration with Mutual Information Maximizing State and Action Embeddings pdf

Sergey Levine, Hyun Oh Song, Yeonwoo Jeong, 2018, ArXiv.

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Sergey Levine, Aviral Kumar, Justin Fu, 2019, NeurIPS.

Real-time prosody-driven synthesis of body language

Sergey Levine, Vladlen Koltun, Christian Theobalt, 2009, ACM Trans. Graph..

Learning Dexterous Manipulation Policies from Experience and Imitation pdf

Sergey Levine, Abhishek Gupta, Vikash Kumar, 2016, ArXiv.

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement pdf

Sergey Levine, Ruslan Salakhutdinov, Xinyang Geng, 2020, NeurIPS.

Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

Sergey Levine, Pieter Abbeel, Gregory Kahn, 2019, 2019 International Conference on Robotics and Automation (ICRA).

Divide-and-Conquer Reinforcement Learning pdf

Sergey Levine, Vikash Kumar, Aravind Rajeswaran, 2017, ICLR.

When to Trust Your Model: Model-Based Policy Optimization

Sergey Levine, Justin Fu, Marvin Zhang, 2019, NeurIPS.

Deep visual foresight for planning robot motion

Sergey Levine, Chelsea Finn, S. Levine, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Diagnosing Bottlenecks in Deep Q-learning Algorithms

Sergey Levine, Aviral Kumar, Justin Fu, 2019, ICML.

Meta-Learning with Implicit Gradients

Sergey Levine, Chelsea Finn, Aravind Rajeswaran, 2019, NeurIPS.

Learning Complex Neural Network Policies with Trajectory Optimization

Sergey Levine, Vladlen Koltun, S. Levine, 2014, ICML.

The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

Andrew Owens, Edward H. Adelson, Sergey Levine, 2017, CoRL.

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

Sergey Levine, Shixiang Gu, Ethan Holly, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Off-Policy Evaluation of Generalization for Deep Q-Learning in BinaryReward Tasks

Sergey Levine, Julian Ibarz, Konstantinos Bousmalis, 2019 .

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning

Michael I. Jordan, S. Levine, I. Stoica, 2018, ArXiv.

Path integral guided policy search

Sergey Levine, Stefan Schaal, Yevgen Chebotar, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Learning Latent Plans from Play

Sergey Levine, Jonathan Tompson, Vikash Kumar, 2019, CoRL.

Data-Efficient Hierarchical Reinforcement Learning

Sergey Levine, Honglak Lee, Ofir Nachum, 2018, NeurIPS.

Unsupervised Learning for Physical Interaction through Video Prediction

Sergey Levine, Chelsea Finn, Ian J. Goodfellow, 2016, NIPS.

Feature Construction for Inverse Reinforcement Learning

Sergey Levine, Vladlen Koltun, Zoran Popovic, 2010, NIPS.

Shared Autonomy via Deep Reinforcement Learning pdf

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2018, Robotics: Science and Systems.

Learning Instance Segmentation by Interaction

Jitendra Malik, Sergey Levine, Trevor Darrell, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Recall Traces: Backtracking Models for Efficient Reinforcement Learning pdf

Sergey Levine, Yoshua Bengio, Hugo Larochelle, 2018, ICLR.

Optimal control with learned local models: Application to dexterous manipulation

Sergey Levine, Vikash Kumar, Emanuel Todorov, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Sergey Levine, Gregory Kahn, Anusha Nagabandi, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Sergey Levine, Kurt Konolige, Mrinal Kalakrishnan, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

Allan Jabri, Sergey Levine, Pieter Abbeel, 2018, ICML.

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2016, ICML.

End-to-End Training of Deep Visuomotor Policies pdf

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2015, J. Mach. Learn. Res..

Learning Visual Servoing with Deep Features and Fitted Q-Iteration pdf

Sergey Levine, Pieter Abbeel, Alex X. Lee, 2017, ICLR.

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2018, Robotics: Science and Systems.

Deep reinforcement learning for tensegrity robot locomotion

Sergey Levine, Pieter Abbeel, Vytas SunSpiral, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Learning contact-rich manipulation skills with guided policy search

Nolan Wagener, Sergey Levine, Pieter Abbeel, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Sergey Levine, Pieter Abbeel, Aurick Zhou, 2018, ICML.

Guided Policy Search

Sergey Levine, Vladlen Koltun, S. Levine, 2013, ICML.

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2017, ICML.

Deep Reinforcement Learning for Robotic Manipulation pdf

Sergey Levine, Shixiang Gu, Ethan Holly, 2016, ArXiv.

Meta-Reinforcement Learning of Structured Exploration Strategies

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2018, NeurIPS.

Learning to Adapt: Meta-Learning for Model-Based Control pdf

Sergey Levine, Ronald S. Fearing, Pieter Abbeel, 2018, ArXiv.

Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm pdf

Sergey Levine, Chelsea Finn, S. Levine, 2017, ICLR.

Unsupervised Perceptual Rewards for Imitation Learning pdf

Sergey Levine, Pierre Sermanet, Kelvin Xu, 2016, Robotics: Science and Systems.

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review pdf

Sergey Levine, S. Levine, 2018, ArXiv.

Stochastic Adversarial Video Prediction pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2018, ArXiv.

Cognitive Mapping and Planning for Visual Navigation

Rahul Sukthankar, James Davidson, Saurabh Gupta, 2017, International Journal of Computer Vision.

Learning modular neural network policies for multi-task and multi-robot transfer

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables pdf

Sergey Levine, Chelsea Finn, Aurick Zhou, 2019, ICML.

PLATO: Policy learning using adaptive trajectory optimization

Sergey Levine, Pieter Abbeel, Tianhao Zhang, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2016, ArXiv.

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models pdf

Sergey Levine, Pieter Abbeel, Bradly C. Stadie, 2015, ArXiv.

(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image pdf

Sergey Levine, Fereshteh Sadeghi, S. Levine, 2016, Robotics: Science and Systems.

Deep spatial autoencoders for visuomotor learning

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

Sergey Levine, Vladlen Koltun, Zoran Popovic, 2011, NIPS.

One-shot learning of manipulation skills with online dynamics adaptation and neural network priors

Sergey Levine, Pieter Abbeel, Justin Fu, 2015, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Sergey Levine, Rowan McAllister, Roberto Calandra, 2018, NeurIPS.

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles pdf

Balaraman Ravindran, Aravind Rajeswaran, Sergey Levine, 2016, ICLR.

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes pdf

Thomas L. Griffiths, Sergey Levine, Trevor Darrell, 2018, ICLR.

Learning to Poke by Poking: Experiential Learning of Intuitive Physics pdf

Jitendra Malik, Sergey Levine, Pieter Abbeel, 2016, NIPS.

Continuous Inverse Optimal Control with Locally Optimal Examples pdf

Sergey Levine, Vladlen Koltun, S. Levine, 2012, ICML.

Soft Actor-Critic Algorithms and Applications pdf

Henry Zhu, Sergey Levine, Sehoon Ha, 2018, ArXiv.

Exploring Deep and Recurrent Architectures for Optimal Control pdf

Sergey Levine, S. Levine, 2013, ArXiv.

Model-based reinforcement learning with parametrized physical models and optimism-driven exploration

Sergey Levine, Pieter Abbeel, Sachin Patil, 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Learning with Latent Language pdf

Dan Klein, Sergey Levine, Jacob Andreas, 2017, NAACL.

PRECOG : PREdictions Conditioned On Goals in Visual Multi-Agent Scenarios

Kris M. Kitani, S. Levine, Rowan McAllister, 2019 .

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

Anca D. Dragan, Sergey Levine, Chelsea Finn, 2018, ICML.

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Henry Zhu, Sergey Levine, Abhishek Gupta, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL pdf

Sergey Levine, Chelsea Finn, Anusha Nagabandi, 2018, ICLR.

Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning pdf

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2017, ICLR.

Space-time planning with parameterized locomotion controllers

Sergey Levine, Vladlen Koltun, Zoran Popovic, 2011, TOGS.

Continuous character control with low-dimensional embeddings

Sergey Levine, Vladlen Koltun, Zoran Popovic, 2012, ACM Trans. Graph..

MELD: Meta-Reinforcement Learning from Images via Latent State Models pdf

Sergey Levine, Chelsea Finn, Anusha Nagabandi, 2020, CoRL.

Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning pdf

Sergey Levine, Marcel Salathé, Scott L. Delp, 2018, ArXiv.

Learning deep neural network policies with continuous memory states

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Diversity is All You Need: Learning Skills without a Reward Function pdf

Sergey Levine, Abhishek Gupta, Julian Ibarz, 2018, ICLR.

DeepMimic

Sergey Levine, Pieter Abbeel, Michiel van de Panne, 2018, ACM Trans. Graph..

Optimism-driven exploration for nonlinear systems

Sergey Levine, Michael I. Jordan, Pieter Abbeel, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Stochastic Variational Video Prediction pdf

Sergey Levine, Dumitru Erhan, Roy H. Campbell, 2017, ICLR.

Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

Sergey Levine, Trevor Darrell, Kate Saenko, 2015, WAFR.

Model-Based Reinforcement Learning for Atari pdf

Sergey Levine, Dumitru Erhan, Roy H. Campbell, 2019, ICLR.

Learning Visual Predictive Models of Physics for Playing Billiards pdf

Jitendra Malik, Sergey Levine, Katerina Fragkiadaki, 2015, ICLR.

Combining self-supervised learning and imitation for vision-based rope manipulation

Jitendra Malik, Sergey Levine, Pieter Abbeel, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

Sergey Levine, Chelsea Finn, Ofir Nachum, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Time-Agnostic Prediction: Predicting Predictable Video Frames pdf

Alexei A. Efros, Sergey Levine, Dinesh Jayaraman, 2018, ICLR.

Gradient Surgery for Multi-Task Learning pdf

Karol Hausman, Abhishek Gupta, Chelsea Finn, 2020, NeurIPS.

Goal-driven dynamics learning via Bayesian optimization

Sergey Levine, Claire J. Tomlin, Somil Bansal, 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads pdf

Rowan McAllister, Roberto Calandra, Gregory Kahn, 2020, IEEE Robotics and Automation Letters.

Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2018, ICLR.

Real-time prosody-driven synthesis of body language

Sergey Levine, Vladlen Koltun, Christian Theobalt, 2009, SIGGRAPH 2009.

Residual Reinforcement Learning for Robot Control

Sergey Levine, Matthias Loskyll, Avinash Kumar, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Unsupervised Meta-Learning for Reinforcement Learning pdf

Sergey Levine, Abhishek Gupta, Chelsea Finn, 2018, ArXiv.

The Ingredients of Real-World Robotic Reinforcement Learning pdf

Sergey Levine, Abhishek Gupta, Vikash Kumar, 2020, ICLR.

Learning from the hindsight plan — Episodic MPC improvement

Sergey Levine, Pieter Abbeel, Tianhao Zhang, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow pdf

Sergey Levine, Pieter Abbeel, Angjoo Kanazawa, 2018, ICLR.

REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning pdf

Sergey Levine, Brian Yang, Dinesh Jayaraman, 2019, ArXiv.

Recurrent Network Models for Human Dynamics

Jitendra Malik, Sergey Levine, Katerina Fragkiadaki, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning pdf

Harini Kannan, Dumitru Erhan, Sergey Levine, 2020, ArXiv.

Gesture controllers

S. Levine, S. Thrun, Philipp Krähenbühl, 2010, ACM Trans. Graph..

Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight pdf

Sergey Levine, Chelsea Finn, Annie Xie, 2019, Robotics: Science and Systems.

Variable-Shot Adaptation for Online Meta-Learning pdf

Sergey Levine, Chelsea Finn, Tianhe Yu, 2020, ArXiv.

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

Sergey Levine, Karol Hausman, Corey Lynch, 2019, CoRL.

Offline policy evaluation across representations with applications to educational games

Sergey Levine, Zoran Popovic, Yun-En Liu, 2014, AAMAS.

MOPO: Model-based Offline Policy Optimization pdf

Lantao Yu, Stefano Ermon, Tengyu Ma, 2020, NeurIPS.

Rearrangement: A Challenge for Embodied AI pdf

Roozbeh Mottaghi, Sonia Chernova, Jia Deng, 2020, ArXiv.

Model Inversion Networks for Model-Based Optimization pdf

Sergey Levine, Aviral Kumar, S. Levine, 2019, NeurIPS.

Learning Human Objectives by Evaluating Hypothetical Behavior

Anca D. Dragan, Shane Legg, Jan Leike, 2019, ICML.

WILDS: A Benchmark of in-the-Wild Distribution Shifts pdf

Jure Leskovec, Sergey Levine, Chelsea Finn, 2020, ArXiv.

Entity Abstraction in Visual Model-Based Reinforcement Learning pdf

Jiajun Wu, Joshua B. Tenenbaum, Chelsea Finn, 2019, CoRL.

SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments

Sergey Levine, Chelsea Finn, Nicholas Rhinehart, 2021, ICLR.

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems pdf

Sergey Levine, Justin Fu, George Tucker, 2020, ArXiv.

Deep Object-Centric Representations for Generalizable Robot Learning

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

SFV

Jitendra Malik, Sergey Levine, Pieter Abbeel, 2018, ACM Trans. Graph..

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games ?

S. Levine, Charlie Beattie, Balaraman Ravindran, 2018 .

Meta-Learning

Sergey Levine, Sham M. Kakade, Chelsea Finn, 2019, Automated Machine Learning.

Generalizing Skills with Semi-Supervised Reinforcement Learning pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2016, ICLR.

Adversarial Policies: Attacking Deep Reinforcement Learning pdf

Sergey Levine, Stuart J. Russell, Neel Kant, 2019, ICLR.

Backprop KF: Learning Discriminative Deterministic State Estimators

Sergey Levine, Anurag Ajay, Pieter Abbeel, 2016, NIPS.

ViNG: Learning Open-World Navigation with Visual Goals pdf

Sergey Levine, Nicholas Rhinehart, Gregory Kahn, 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments pdf

Sergey M. Plis, Helge J. Ritter, Sergey Levine, 2018, ArXiv.

Inverse Optimal Control for Humanoid Locomotion

S. Levine, T. Park, 2013 .

Reasoning About Physical Interactions with Object-Centric Models

S. Levine, J. Tenenbaum, W. Freeman, 2018 .

Emergent Social Learning via Multi-agent Reinforcement Learning

Natasha Jaques, Sergey Levine, Douglas Eck, 2020, ICML.

Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes

Thomas L. Griffiths, Michael Chang, Sergey Levine, 2019 .

Cognitive Mapping and Planning for Visual Navigation Supplementary Material

S. Levine, R. Sukthankar, Jitendra Malik, 2017 .

ERATING OFFLINE REINFORCEMENT LEARNING

S. Levine, Ofir Nachum, Aviral Kumar, 2021 .

Fully Autonomous Real-World Reinforcement Learning for Mobile Manipulation

S. Levine, Abhishek Gupta, Brian Yang, 2021, 2107.13545.

Multi-Task Offline Reinforcement Learning with Conservative Data Sharing

S. Levine, Karol Hausman, Chelsea Finn, 2021 .

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

S. Levine, Aviral Kumar, Xinyang Geng, 2022, ICML.

Accelerating Online Reinforcement Learning with Offline Datasets

S. Levine, Abhishek Gupta, Ashvin Nair, 2020, ArXiv.

Gradient Surgery for Multi-Task Learning

S. Levine, Saurabh Kumar, Abhishek Gupta, 2020, NeurIPS.

Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

S. Levine, Ken Goldberg, Rowan McAllister, 2019, IEEE Robotics and Automation Letters.

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

S. Levine, G. Tucker, Aviral Kumar, 2020, ArXiv.

Reinforcement Learning with Videos: Combining Offline Observations with Interaction

S. Levine, Kostas Daniilidis, Chelsea Finn, 2020, CoRL.

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

S. Levine, Igor Mordatch, Michael Janner, 2020, ArXiv.

ICLR 2019 Expert Replay Buffer ActorEnvironment Discriminator Policy Replay Buffer Absorbing State Wrapper s a Critic

Kumar Krishna Agrawal, S. Levine, Jonathan Tompson, 2019 .

Guided Meta-Policy Search

S. Levine, P. Abbeel, Abhishek Gupta, 2019, NeurIPS.

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

Kumar Krishna Agrawal, S. Levine, Jonathan Tompson, 2018, 1809.02925.

Conservative Q-Learning for Offline Reinforcement Learning

S. Levine, G. Tucker, Aviral Kumar, 2020, NeurIPS.

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

Mengjiao Yang, S. Levine, Justin Fu, 2022, NAACL-HLT.

SKEW-FIT: STATE-COVERING SELF-SUPERVISED RE-

S. Levine, Ashvin Nair, Shikhar Bahl, 2019 .

Learning To Reach Goals Without Reinforcement Learning

S. Levine, Abhishek Gupta, Benjamin Eysenbach, 2019, ArXiv.

Universal Planning Networks-Long Version + Supplementary

S. Levine, P. Abbeel, A. Srinivas, 2018 .

GOAL-CONDITIONED POLICIES

S. Levine, Dibya Ghosh, Abhishek Gupta, 2018 .

Learning Torque-Driven Manipulation Primitives with a Multilayer Neural Network

S. Levine, P. Abbeel, Nolan Wagener, 2014 .

MENT LEARNING OVER BEHAVIORAL CLONING?

S. Levine, Aviral Kumar, Joey Hong, 2022 .

SHOULD I RUN OFFLINE REINFORCEMENT LEARNING

S. Levine, Aviral Kumar, Joey Hong, 2021 .

Extending the WILDS Benchmark for Unsupervised Adaptation

Tatsunori B. Hashimoto, Pang Wei Koh, Sang Michael Xie, 2021, ICLR.

SMiRL: Surprise Minimizing Reinforcement Learning in Dynamic Environments

S. Levine, Chelsea Finn, Nicholas Rhinehart, 2019 .

The Full-Time Guild Master

S. Levine, 2008 .

S Tochastic V Ariational V Ideo P Rediction

S. Levine, D. Erhan, R. Campbell, 2018 .

Scenario A : Full Information Scenario B : Incomplete Information policy

Dinesh Jayaraman, S. Levine, Dinesh Jayaraman, 2019 .

ADAIL: Adaptive Adversarial Imitation Learning

S. Levine, Balaraman Ravindran, 2020 .

Learning with Humans in the Loop

S. Levine, A. Dragan, Gokul Swamy, 2020 .

EWARDS FOR I MITATION L EARNING

S. Levine, Pierre Sermanet, Kelvin Xu, 2017 .

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

S. Levine, P. Abbeel, X. B. Peng, 2018, ICLR.

Predict futures using video prediction model Training Time Test Time Collect Kinesthetic Demonstrations Autonomous Random Data

S. Levine, F. Ebert, Chelsea Finn, 2022 .

Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning

S. Levine, P. Abbeel, Abhishek Gupta, 2017, ICLR.

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

John D. Co-Reyes, S. Levine, Justin Fu, 2017, NIPS.

MODELING BODY LANGUAGE FROM SPEECH IN NATURAL CONVERSATION A RESEARCH REPORT SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

S. Levine, 2009 .

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration

Rouhollah Rahmatizadeh, Ladislau Bölöni, Sergey Levine, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Learning Locomotion Primitives from Contextual Bayesian Optimization

S. Levine, Brian Yang, R. Calandra, 2018 .

DeepMimic

S. Levine, P. Abbeel, X. B. Peng, 2018, ACM Transactions on Graphics.

Interferobot: aligning an optical interferometer by a reinforcement learning agent

S. Levine, Vincent Vanhoucke, Hugo Ponte, 2020 .

Adaptive Risk Minimization: Learning to Adapt to Domain Shift

S. Levine, Abhishek Gupta, Chelsea Finn, 2020, NeurIPS.

Ensemble Policy Optimization for Learning Robust Policies

S. Levine, Balaraman Ravindran, A. Rajeswaran, 2017 .

Collective robot reinforcement learning with distributed asynchronous guided policy search

Sergey Levine, Yevgen Chebotar, Mrinal Kalakrishnan, 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Guided Meta-Policy Search

Sergey Levine, Pieter Abbeel, Abhishek Gupta, 2019, NeurIPS.

Decoupled Meta-Learning with Structured Latents

S. Levine, Chelsea Finn, Russell Mendonca, 2019 .

Learning Powerful Policies by Using Consistent Dynamics Model pdf

Sergey Levine, Yoshua Bengio, Tristan Deleu, 2019, ArXiv.

Offline Meta-Reinforcement Learning for Industrial Insertion pdf

Oleg O. Sushkov, Sergey Levine, Stefan Schaal, 2021, ArXiv.

Regret Minimization for Partially Observable Deep Reinforcement Learning pdf

Kurt Keutzer, Sergey Levine, Peter H. Jin, 2017, ICML.

Skew-Fit: State-Covering Self-Supervised Reinforcement Learning pdf

Sergey Levine, Ashvin Nair, Shikhar Bahl, 2019, ICML.

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following pdf

Sergey Levine, Sergio Guadarrama, Justin Fu, 2019, ICLR.

Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation

Sergey Levine, Pierre Sermanet, Corey Lynch, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Conservative Objective Models for Effective Offline Model-Based Optimization

Sergey Levine, Aviral Kumar, Brandon Trabucco, 2021, ICML.

Deep Dynamics Models for Learning Dexterous Manipulation pdf

Sergey Levine, Vikash Kumar, Anusha Nagabandi, 2019, CoRL.

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

Sergey Levine, Pieter Abbeel, Xue Bin Peng, 2019, NeurIPS.

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

S. Weinberg, S. Levine, T. Griffiths, 2020, ICML.

An Empirical Exploration of AI Safety

S. Levine, A. Dragan, 2017 .

BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY

S. Levine, 2009 .

ROLE OF TWO LEARNING RATES IN CONVERGENCE OF MODEL-AGNOSTIC META-LEARNING

S. Levine, 2019 .

OUT-OF-DISTRIBUTION TASKS

S. Levine, 2019 .

Conditional Networks for Few-Shot Semantic Segmentation

Alexei A. Efros, Sergey Levine, Trevor Darrell, 2018, ICLR.

Manipulation by Feel: Touch-Based Control with Deep Predictive Models

Sergey Levine, Chelsea Finn, Stephen Tian, 2019, 2019 International Conference on Robotics and Automation (ICRA).

RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models

Sergey Levine, Nicholas Rhinehart, Gregory Kahn, 2021, CoRL.

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

S. Levine, D. Erhan, M. Babaeizadeh, 2022, ICLR.

Reasoning About Physical Interactions with Object-Oriented Prediction and Planning pdf

Sergey Levine, Jiajun Wu, Joshua B. Tenenbaum, 2018, ICLR.

Explore and Control with Adversarial Surprise pdf

Sergey Levine, Natasha Jaques, Stuart J. Russell, 2021, ArXiv.

MBMF: Model-Based Priors for Model-Free Reinforcement Learning pdf

Sergey Levine, Claire J. Tomlin, Somil Bansal, 2017, ArXiv.

Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning

Sergey Levine, Kristofer S. J. Pister, Daniel S. Drew, 2019, IEEE Robotics and Automation Letters.

FitVid: Overfitting in Pixel-Level Video Prediction pdf

Sergey Levine, Dumitru Erhan, Chelsea Finn, 2021, ArXiv.

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Thomas L. Griffiths, Sergey Levine, Michael Chang, 2021, ICML.

Conservative Q-Learning for Offline Reinforcement Learning pdf

George Tucker, Aviral Kumar, Sergey Levine, 2020, NeurIPS.

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning pdf

Sergey Levine, Nicholas Rhinehart, Avi Singh, 2020, ICLR.

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Reward Signals

Sergey Levine, Eugen Solowjow, Jianlan Luo, 2019 .

Data-Driven Offline Optimization For Architecting Hardware Accelerators pdf

Sergey Levine, Aviral Kumar, Kevin Swersky, 2021, ArXiv.

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives pdf

Sergey Levine, Yoshua Bengio, Jonathan Binas, 2019, ICLR.

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2019, ICLR.

CoMPS: Continual Meta Policy Search pdf

Sergey Levine, Chelsea Finn, Glen Berseth, 2021, ICLR.

Learning 2 D Linear Dynamics in Image Space Using Neural Networks

J. Schulman, S. Levine, P. Abbeel, 2014 .

Learned Tactile Dynamics Candidate actions Sequence Prediction Compute Costs Choose Best Action Model Predictive Control

Sergey Levine, Chelsea Finn, Stephen Tian, 2019 .

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

Sergey Levine, Brian Ichter, Alexander Toshev, 2021, ArXiv.

Benchmarks for Deep Off-Policy Evaluation pdf

Sergey Levine, Mohammad Norouzi, Justin Fu, 2021, ICLR.

Offline Meta-Reinforcement Learning with Online Self-Supervision pdf

Sergey Levine, Ashvin Nair, Laura Smith, 2021, ArXiv.

Persistent Reinforcement Learning via Subgoal Curricula pdf

Sergey Levine, Karol Hausman, Chelsea Finn, 2021, ArXiv.

Multi-Robot Deep Reinforcement Learning for Mobile Navigation pdf

Sergey Levine, Gregory Kahn, Katie Kang, 2021, ArXiv.

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

Sergey Levine, Vincent Vanhoucke, Eric Jang, 2018, CoRL.

Amortized Conditional Normalized Maximum Likelihood pdf

Sergey Levine, Aurick Zhou, S. Levine, 2020, ArXiv.

Bayes Net

Dan Klein, Sergey Levine, Pieter Abbeel, 2017, Encyclopedia of Machine Learning and Data Mining.

Learning Dynamic Manipulation Skills under Unknown Dynamics with Guided Policy Search

S. Levine, P. Abbeel, 2014 .

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

Sergey Levine, Rowan McAllister, Jeff Clune, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Extended Abstract: Concept Acquisition Through Meta-Learning

Joshua T. Abbott, Joshua C. Peterson, S. Levine, 2017 .

Actual SystemCurrent Linear Dynamics Controller Cost Function Bayesian Optimization Cost Evaluator Output Cost New Linear Dynamics Optimal Control Scheme

S. Levine, C. Tomlin, R. Calandra, 2018 .

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

S. Levine, Deirdre Quillen, Karol Hausman, 2019, CoRL.

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

S. Levine, D. Erhan, Chelsea Finn, 2019, ICLR.

Learning Agile Robotic Locomotion Skills by Imitating Animals

S. Levine, Jie Tan, X. B. Peng, 2020, Robotics: Science and Systems.

SMiRL: Surprise Minimizing RL in Entropic Environments

Sergey Levine, Chelsea Finn, Glen Berseth, 2019 .

InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck

Sergey Levine, Yoshua Bengio, Hugo Larochelle, 2019 .

Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery

S. Levine, Tuomas Haarnoja, Xinyang Geng, 2019, ICLR.

Control-Aware Prediction Objectives for Autonomous Driving

S. Levine, Rowan McAllister, Adrien Gaidon, 2022, 2022 International Conference on Robotics and Automation (ICRA).

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

S. Levine, Dhruv Shah, 2022, ArXiv.

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

S. Levine, Abhishek Gupta, Vikash Kumar, 2019, CoRL.

Maximum Entropy RL (Provably) Solves Some Robust RL Problems

S. Levine, Benjamin Eysenbach, 2021, ICLR.

Meta-Learning without Memorization

S. Levine, G. Tucker, Chelsea Finn, 2019, ICLR.

Learning Invariant Representations for Reinforcement Learning without Reconstruction

S. Levine, Rowan McAllister, R. Calandra, 2020, ICLR.

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

S. Levine, Benjamin Eysenbach, Ilya Kostrikov, 2021, ICLR.

Simple and Effective VAE Training with Calibrated Decoders

Sergey Levine, Kostas Daniilidis, Oleh Rybkin, 2020, ICML.

Outcome-Driven Reinforcement Learning via Variational Inference pdf

Sergey Levine, Rowan McAllister, Yarin Gal, 2021, NeurIPS.

A UTONOMOUS R EINFORCEMENT L EARNING : F ORMALISM AND B ENCHMARKING

S. Levine, Archit Sharma, Karol Hausman, 2022 .

Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling

Sergey Levine, Chelsea Finn, Xinyang Geng, 2019 .

ASE

S. Levine, X. B. Peng, S. Fidler, 2022, ACM Trans. Graph..

AMP

S. Levine, P. Abbeel, X. B. Peng, 2021, ACM Trans. Graph..

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

S. Levine, A. Dragan, S. Reddy, 2022, ArXiv.

Combining Model-based and Model-free RL via Multi-step Control Variates

Sergey Levine, Yoshua Bengio, Yuchen Lu, 2018 .

Planning with Diffusion for Flexible Behavior Synthesis

S. Levine, J. Tenenbaum, Michael Janner, 2022, 2205.09991.

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Amrith Rajagopal Setlur, S. Levine, Benjamin Eysenbach, 2022, NeurIPS.

Multimodal Masked Autoencoders Learn Transferable Representations

S. Levine, P. Abbeel, Xinyang Geng, 2022, ArXiv.

Gesture controllers

Sergey Levine, Sebastian Thrun, Vladlen Koltun, 2010, ACM Trans. Graph..

O BJECT -C ENTRIC L EARNING AS N ESTED O PTIMIZATION

S. Levine, T. Griffiths, Michael Chang, 2022 .

Contrastive Learning as Goal-Conditioned Reinforcement Learning

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ArXiv.

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

S. Levine, T. Griffiths, Michael Chang, 2022, ArXiv.

SFV

S. Levine, P. Abbeel, J. Malik, 2018, ACM Transactions on Graphics.

DeepMimic

S. Levine, P. Abbeel, X. B. Peng, 2018, ACM Transactions on Graphics.

Learning to Manipulate Granular Media with a Robot

Sergey Levine, Jonathan Tompson, Connor Schenck, 2017 .

Adaptive Adversarial Imitation Learning

Jonathan Tompson, Sergey Levine, Yiren Lu, 2019 .

Mint: Matrix-Interleaving for Multi-Task Learning

Sergey Levine, Karol Hausman, Abhishek Gupta, 2019 .

Offline RL Policies Should be Trained to be Adaptive

S. Levine, Dibya Ghosh, Pulkit Agrawal, 2022, ArXiv.

Meta-Learning to Guide Segmentation

Alexei A. Efros, Sergey Levine, Trevor Darrell, 2018 .

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

Jason J. Choi, S. Levine, C. Tomlin, 2022, ICML.

Inner Monologue: Embodied Reasoning through Planning with Language Models

Peter R. Florence, S. Levine, Pierre Sermanet, 2022, CoRL.

Offline RL for Natural Language Generation with Implicit Language Q Learning

S. Levine, Ilya Kostrikov, C. Snell, 2022, ArXiv.

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

Sergey Levine, Siddharth Reddy, Karunesh Ganguly, 2022, ICLR.

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

Sergey Levine, Alex Lamb, Anirudh Goyal, 2021, ICLR.

Learning Latent Plans from Play

S. Levine, Pierre Sermanet, Vikash Kumar, 2019, CoRL.

Offline Meta-Reinforcement Learning with Online Self-Supervision

Vitchyr H. Pong, S. Levine, Laura Smith, 2021, ICML.

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

S. Levine, Aviral Kumar, Avi Singh, 2022, 2207.04703.

Goal-Conditioned Video Prediction

Sergey Levine, Chelsea Finn, Oleh Rybkin, 2019 .

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

S. Levine, Aviral Kumar, Avi Singh, 2022, CoRL.

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

S. Levine, Ashvin Nair, Kuan Fang, 2022, ArXiv.

P LANNING TO P RACTICE : E FFICIENT O NLINE F INE T UNING BY C OMPOSING G OALS IN L ATENT S PACE

S. Levine, Ashvin Nair, Patrick Yin, 2022 .

SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning

C. K. Liu, S. Levine, Jie Tan, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

ASE

S. Levine, X. B. Peng, S. Fidler, 2022, ACM Transactions on Graphics.

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

S. Levine, B. Osinski, Brian Ichter, 2022, CoRL.

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

S. Levine, Ashvin Nair, Amy Zhang, 2022, ICML.

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

S. Levine, Laura Smith, Ilya Kostrikov, 2023, Robotics: Science and Systems.

Autonomous Reinforcement Learning: Formalism and Benchmarking

S. Levine, Archit Sharma, Karol Hausman, 2021, ICLR.

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

S. Levine, Pierre Sermanet, Vincent Vanhoucke, 2022, CoRL.

TRAIL: N EAR -O PTIMAL I MITATION L EARNING WITH S UBOPTIMAL D ATA

Mengjiao Yang, S. Levine, Mengjiao Yang, 2021 .

RoboNet: Large-Scale Multi-Robot Learning

S. Levine, F. Ebert, Chelsea Finn, 2019, CoRL.

What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning

S. Levine, A. Dragan, S. Reddy, 2018 .

Offline RL Policies Should be Trained to be Adaptive

S. Levine, Dibya Ghosh, Pulkit Agrawal, 2022, ICML.

Should I Run Offline Reinforcement Learning or Behavioral Cloning?

S. Levine, Aviral Kumar, Joey Hong, 2022, ICLR.

Offline RL for Natural Language Generation with Implicit Language Q Learning

S. Levine, Ilya Kostrikov, C. Snell, 2022, ArXiv.

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

S. Levine, T. Griffiths, Michael Chang, 2022, ArXiv.

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ArXiv.

You Only Live Once: Single-Life Reinforcement Learning

Annie S. Chen, S. Levine, Archit Sharma, 2022, NeurIPS.

Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks

S. Levine, Ashvin Nair, Kuan Fang, 2022, CoRL.

MEMO: Test Time Robustness via Adaptation and Augmentation

S. Levine, Chelsea Finn, Marvin Zhang, 2021, NeurIPS.

Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience

S. Levine, Natasha Jaques, Marwa Abdulhai, 2022, ArXiv.

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

S. Levine, P. Abbeel, Igor Mordatch, 2022, ArXiv.

GNM: A General Navigation Model to Drive Any Robot

S. Levine, Dhruv Shah, N. Hirose, 2022, ArXiv.

GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots

S. Levine, X. B. Peng, K. Sreenath, 2022, CoRL.

Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision

S. Levine, Ashvin Nair, Eugen Solowjow, 2022, 2210.15206.

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

S. Levine, A. Dragan, S. Reddy, 2022, NeurIPS.

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

S. Levine, Dhruv Shah, 2022, Robotics: Science and Systems XVIII.

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

S. Levine, F. Ebert, Chelsea Finn, 2022, ArXiv.

Contrastive Learning as Goal-Conditioned Reinforcement Learning

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ArXiv.

Adversarial Policies Beat Professional-Level Go AIs

S. Levine, A. Gleave, Michael Dennis, 2022, ArXiv.

Intuitive Physics Modelling for Real-World Object Interactions

S. Levine, Pierre Sermanet, Yevgen Chebotar, 2022 .

Data-Driven Offline Decision-Making via Invariant Representation Learning

S. Levine, Aviral Kumar, Yi-Hsun Su, 2022, NeurIPS.

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement

S. Levine, T. Griffiths, Franziska Meier, 2023, ICLR.

RoboNet: Large-Scale Multi-Robot Learning

S. Levine, F. Ebert, Chelsea Finn, 2019, Conference on Robot Learning.

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

S. Levine, Vincent Vanhoucke, M. Ryoo, 2022, Robotics: Science and Systems.

O BJECT R EPRESENTATIONS AS E QUILIBRIA : T RAINING I TERATIVE I NFERENCE A LGORITHMS WITH I MPLICIT D IFFERENTIATION

S. Levine, T. Griffiths, Michael Chang, 2022 .

Multi-Task Imitation Learning for Linear Dynamical Systems

S. Levine, C. Tomlin, Stephen Tu, 2022, ArXiv.

Learning robotic navigation from experience: principles, methods and recent results

S. Levine, Dhruv Shah, 2022, Philosophical Transactions of the Royal Society B.

Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning

Anca D. Dragan, Sergey Levine, Chelsea Finn, 2018 .

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

S. Levine, T. Griffiths, Michael Chang, 2022, NeurIPS.

Understanding the Complexity Gains of Single-Task RL with a Curriculum

S. Levine, Yuexiang Zhai, Yi Ma, 2022, ArXiv.

Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity

S. Levine, S. Kakade, Abhishek Gupta, 2022, NeurIPS.

Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

S. Levine, Rishabh Agarwal, G. Tucker, 2022, ArXiv.

Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

S. Levine, Karol Hausman, Ted Xiao, 2022, 2211.11736.

Imitating Past Successes can be Very Suboptimal

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ArXiv.

Evolving Reinforcement Learning Algorithms

Quoc V. Le, John D. Co-Reyes, S. Levine, 2021, ICLR.

Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents

Sergey Levine, Chelsea Finn, Dinesh Jayaraman, 2019 .

SQIL: Imitation Learning via Regularized Behavioral Cloning pdf

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2019, ArXiv.

Hierarchical Deep Reinforcement Learning Agent with Counter Self-play on Competitive Games

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2018 .

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

Sergey Levine, Nicholas Rhinehart, Rowan McAllister, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Which Mutual-Information Representation Learning Objectives are Sufficient for Control? pdf

Sergey Levine, Kate Rakelly, Carlos Florensa, 2021, NeurIPS.

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability pdf

Sergey Levine, Ryan P. Adams, Aviral Kumar, 2021, NeurIPS.

Recurrent Independent Mechanisms pdf

Sergey Levine, Bernhard Schölkopf, Yoshua Bengio, 2019, ICLR.

Unsupervised Curricula for Visual Meta-Reinforcement Learning

Allan Jabri, Alexei A. Efros, Sergey Levine, 2019, NeurIPS.

More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch

Jitendra Malik, Andrew Owens, Edward H. Adelson, 2018, IEEE Robotics and Automation Letters.

Multi-Task Imitation Learning for Linear Dynamical Systems

S. Levine, C. Tomlin, Stephen Tu, 2022, ArXiv.

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data pdf

Sergey Levine, Ofir Nachum, Mengjiao Yang, 2021, ICLR.

Learning Social Learning

S. Levine, Natasha Jaques, D. Eck, 2020 .

Multi-agent Social Reinforcement Learning Improves Generalization pdf

Natasha Jaques, Sergey Levine, Douglas Eck, 2020, ArXiv.

Causal Confusion in Imitation Learning

Sergey Levine, Dinesh Jayaraman, Pim de Haan, 2019, NeurIPS.

Fully Online Meta-Learning Without Task Boundaries

S. Levine, Jathushan Rajasegaran, Chesea Finn, 2022, ArXiv.

ACCELERATING ONLINE REINFORCEMENT LEARNING VIA MODEL-BASED META-LEARNING

John D. Co-Reyes, S. Levine, G. Berseth, 2021 .

Few-Shot Segmentation Propagation with Guided Networks pdf

Alexei A. Efros, Sergey Levine, Trevor Darrell, 2018, ArXiv.

Self-Supervised Visual Planning with Temporal Skip Connections

Sergey Levine, Chelsea Finn, Alex X. Lee, 2017, CoRL.

Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search

Sergey Levine, Pieter Abbeel, Tianhao Zhang, 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Information is Power: Intrinsic Control via Information Capture pdf

Sergey Levine, Chelsea Finn, Glen Berseth, 2021, NeurIPS.

Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments

John D. Co-Reyes, S. Levine, Chelsea Finn, 2021 .

SMiRL: Surprise Minimizing RL in Dynamic Environments pdf

Sergey Levine, Chelsea Finn, Glen Berseth, 2019, ArXiv.

Unsupervised Learning via Meta-Learning pdf

Sergey Levine, Chelsea Finn, Kyle Hsu, 2018, ICLR.

Learning Image-Conditioned Dynamics Models for Control of Underactuated Legged Millirobots

Sergey Levine, Ronald S. Fearing, Ravi Pandya, 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Neural Network Dynamics Models for Control of Under-actuated Legged Millirobots pdf

Sergey Levine, Ronald S. Fearing, Gregory Kahn, 2017, ArXiv.

How to train your robot with deep reinforcement learning: lessons we have learned

Sergey Levine, Chelsea Finn, Mrinal Kalakrishnan, 2021, Int. J. Robotics Res..

Learning Predictive Models From Observation and Interaction

Sergey Levine, Chelsea Finn, Kostas Daniilidis, 2019, ECCV.

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2020, ICLR.

Variational Policy Search via Trajectory Optimization

Sergey Levine, Vladlen Koltun, S. Levine, 2013, NIPS.

ViNG: Learning Open-World Navigation with Visual Goals

S. Levine, G. Kahn, Nicholas Rhinehart, 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Sergey Levine, Ruslan Salakhutdinov, Benjamin Eysenbach, 2019, NeurIPS.

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Sang Michael Xie, Berton A. Earnshaw, 2020, ICML.

Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation pdf

Sergey Levine, Pieter Abbeel, Gregory Kahn, 2018, CoRL.

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

Sergey Levine, Chelsea Finn, Alex X. Lee, 2018, CoRL.

Predictive Visual Models of Physics for Playing Billiards

S. Levine, Jitendra Malik, Katerina Fragkiadaki, 2015 .

Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models

S. Levine, P. Abbeel, Igor Mordatch, 2022, 2210.13432.

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills pdf

Sergey Levine, Yevgen Chebotar, Karol Hausman, 2021, ICML.

Universal Planning Networks pdf

Allan Jabri, Sergey Levine, Pieter Abbeel, 2018, ICML.

Pretraining strategies for effective promoter-driven gene expression prediction

Aniketh Janardhan Reddy, Nilah M. Ioannidis, Amy X. Lu, 2023, bioRxiv.

GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images

Sergey Levine, Avi Singh, Larry Yang, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Conservative Safety Critics for Exploration pdf

Nicholas Rhinehart, Animesh Garg, Florian Shkurti, 2020, ICLR.

AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos pdf

Pieter Abbeel, Marvin Zhang, Sergey Levine, 2019, Robotics: Science and Systems.

Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

S. Levine, A. Dragan, S. Reddy, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

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

Sergey Levine, Yevgen Chebotar, Karol Hausman, 2021, CoRL.

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning pdf

Sergey Levine, Justin Fu, Katie Luo, 2017, ICLR 2017.

Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

Koushil Sreenath, Sergey Levine, Pieter Abbeel, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

C-Learning: Learning to Achieve Goals via Recursive Classification pdf

Sergey Levine, Ruslan Salakhutdinov, Benjamin Eysenbach, 2020, ICLR.

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model pdf

Sergey Levine, Pieter Abbeel, Alex X. Lee, 2019, NeurIPS.

Efficient Exploration via State Marginal Matching pdf

Sergey Levine, Eric Xing, Ruslan Salakhutdinov, 2019, ArXiv.

Visual Reinforcement Learning with Imagined Goals

Sergey Levine, Ashvin Nair, Shikhar Bahl, 2018, NeurIPS.

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Sergey Levine, Jennifer Hicks, Christopher G. Atkeson, 2020, Journal of NeuroEngineering and Rehabilitation.

PaLM-E: An Embodied Multimodal Language Model

Mehdi S. M. Sajjadi, Peter R. Florence, S. Levine, 2023, ICML.

Accelerating Human Learning with Deep Reinforcement Learning

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2017 .

Automatically Composing Representation Transformations as a Means for Generalization pdf

Thomas L. Griffiths, Sergey Levine, Abhishek Gupta, 2018, ICLR.

Modular Multitask Reinforcement Learning with Policy Sketches pdf

Dan Klein, Sergey Levine, Jacob Andreas, 2016, ICML.

Probabilistic Model-Agnostic Meta-Learning

Sergey Levine, Chelsea Finn, Kelvin Xu, 2018, NeurIPS.

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

Yoshua Bengio, S. Levine, C. Blundell, 2020, ArXiv.

Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads

S. Levine, R. Calandra, G. Kahn, 2020, IEEE Robotics and Automation Letters.

Learning Human Objectives by Evaluating Hypothetical Behavior

S. Levine, S. Legg, A. Dragan, 2019, ICML.

Simple and Effective VAE Training with Calibrated Decoders

S. Levine, Kostas Daniilidis, Oleh Rybkin, 2020, ICML.

EMI: Exploration with Mutual Information

Sergey Levine, Hyun Oh Song, Hyoungseok Kim, 2018, ICML.

Learning Actionable Representations with Goal-Conditioned Policies pdf

Sergey Levine, Abhishek Gupta, Dibya Ghosh, 2018, ICLR.

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

Justin Fu, John D. Co-Reyes, Sergey Levine, 2017, NIPS.

Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments pdf

Sergey Levine, Trevor Darrell, Kate Saenko, 2015, ArXiv.

Planning with Diffusion for Flexible Behavior Synthesis

S. Levine, J. Tenenbaum, Michael Janner, 2022, ICML.

If MaxEnt RL is the Answer, What is the Question? pdf

Sergey Levine, Benjamin Eysenbach, S. Levine, 2019, ArXiv.

2022 Source Labeled Unlabeled Belgium France Norway Validation China Target United States Canada UK Japan Mexico Extra

Pang Wei Koh, Sang Michael Xie, Ananya Kumar, 2022 .

BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

S. Levine, P. Abbeel, G. Kahn, 2020, IEEE Robotics and Automation Letters.

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

S. Levine, Dhruv Shah, 2022, Robotics: Science and Systems.

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation

Sergey Levine, Glen Berseth, Brian Yang, 2021, CoRL.

Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots

Sergey Levine, Gregory Kahn, Katie Kang, 2021, CoRL.

Model-Based Visual Planning with Self-Supervised Functional Distances pdf

Chelsea Finn, Stephen Tian, Suraj Nair, 2020, ICLR.

LaND: Learning to Navigate From Disengagements pdf

Pieter Abbeel, Sergey Levine, Gregory Kahn, 2020, IEEE Robotics and Automation Letters.

ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors pdf

Sergey Levine, Glen Berseth, Brian Yang, 2021, ArXiv.

Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning

S. Levine, Karol Hausman, Corey Lynch, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

A Workflow for Offline Model-Free Robotic Reinforcement Learning

Sergey Levine, Chelsea Finn, Aviral Kumar, 2021, CoRL.

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning pdf

Sergey Levine, Aviral Kumar, Avi Singh, 2020, ArXiv.

One-Shot Visual Imitation Learning via Meta-Learning

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2017, CoRL.

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

S. Levine, Ofir Nachum, Aviral Kumar, 2020, ICLR.

Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

S. Levine, S. Gu, Honglak Lee, 2019, ArXiv.

Latent Space Policies for Hierarchical Reinforcement Learning

Sergey Levine, Pieter Abbeel, Tuomas Haarnoja, 2018, ICML.

Cognitive Mapping and Planning for Visual Navigation

Rahul Sukthankar, James Davidson, Saurabh Gupta, 2017, International Journal of Computer Vision.

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

David D. Fan, Henry A. Leopold, S. Levine, 2021, 2022 International Conference on Robotics and Automation (ICRA).

ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation

S. Levine, Dhruv Shah, N. Hirose, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Visual Memory for Robust Path Following

Jitendra Malik, Sergey Levine, Saurabh Gupta, 2018, NeurIPS.

COMBO: Conservative Offline Model-Based Policy Optimization pdf

Sergey Levine, Chelsea Finn, Aravind Rajeswaran, 2021, NeurIPS.

D4RL: Datasets for Deep Data-Driven Reinforcement Learning pdf

Justin Fu, George Tucker, Ofir Nachum, 2020, ArXiv.

How to Leverage Unlabeled Data in Offline Reinforcement Learning

S. Levine, Karol Hausman, Chelsea Finn, 2022, ICML.

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning pdf

Sergey Levine, Yevgen Chebotar, Karol Hausman, 2021, NeurIPS.

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

S. Levine, Karol Hausman, Chelsea Finn, 2021, ArXiv.

Offline Reinforcement Learning with Implicit Q-Learning pdf

Sergey Levine, Ashvin Nair, Ilya Kostrikov, 2021, ICLR.

Offline Meta-Reinforcement Learning for Industrial Insertion

Oleg O. Sushkov, S. Levine, S. Schaal, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Learning to Reach Goals via Iterated Supervised Learning

Sergey Levine, Justin Fu, Coline Devin, 2019, ICLR.

Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision

S. Levine, Ashvin Nair, Eugen Solowjow, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

S. Levine, Aviral Kumar, Joey Hong, 2022, ArXiv.

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation pdf

Sergey Levine, Alexander Herzog, Mrinal Kalakrishnan, 2018, CoRL.

End-to-End Robotic Reinforcement Learning without Reward Engineering pdf

Sergey Levine, Chelsea Finn, Avi Singh, 2019, Robotics: Science and Systems.

Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling pdf

Sergey Levine, Chelsea Finn, Xinyang Geng, 2020, ArXiv.

What Can I Do Here? Learning New Skills by Imagining Visual Affordances

Sergey Levine, Ashvin Nair, Alexander Khazatsky, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Continual Learning of Control Primitives: Skill Discovery via Reset-Games pdf

Sergey Levine, Chelsea Finn, Kelvin Xu, 2020, NeurIPS.

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

Karol Hausman, Sergey Levine, Vikash Kumar, 2020, Robotics: Science and Systems.

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Sergey Levine, Amy Zhang, S. Levine, 2022, International Conference on Machine Learning.

Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning

Sergey Levine, Jongwook Choi, Shixiang Shane Gu, 2021, ICML.

Planning with Goal-Conditioned Policies

Sergey Levine, Vitchyr Pong, Vitchyr H. Pong, 2019, NeurIPS.

Contextual Imagined Goals for Self-Supervised Robotic Learning

Sergey Levine, Glen Berseth, Ashvin Nair, 2019, CoRL.

Compositional Plan Vectors

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2019, NeurIPS.

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Sergey Levine, Nicholas Rhinehart, Rowan McAllister, 2020, ICML.

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

Amrith Rajagopal Setlur, Annie S. Chen, S. Levine, 2023, ArXiv.

Learning to Walk via Deep Reinforcement Learning pdf

Sergey Levine, Sehoon Ha, Jie Tan, 2018, Robotics: Science and Systems.

Deep Imitative Models for Flexible Inference, Planning, and Control pdf

Sergey Levine, Nicholas Rhinehart, Rowan McAllister, 2018, ICLR.

Offline Reinforcement Learning for Visual Navigation

S. Levine, Nicholas Rhinehart, Dhruv Shah, 2022, CoRL.

Robust Predictable Control pdf

Sergey Levine, Ruslan Salakhutdinov, Benjamin Eysenbach, 2021, NeurIPS.

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design pdf

Natasha Jaques, Stuart J. Russell, Andrew Critch, 2020, NeurIPS.

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

S. Levine, A. Dragan, S. Reddy, 2022, 2022 International Conference on Robotics and Automation (ICRA).

Model-Based Reinforcement Learning via Latent-Space Collocation pdf

Sergey Levine, Oleh Rybkin, Igor Mordatch, 2021, ICML.

Assisted Perception: Optimizing Observations to Communicate State pdf

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2020, CoRL.

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

S. Levine, F. Ebert, Chelsea Finn, 2022, Robotics: Science and Systems.

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification pdf

Sergey Levine, Ruslan Salakhutdinov, Benjamin Eysenbach, 2021, NeurIPS.

Offline Meta-Reinforcement Learning with Advantage Weighting

Sergey Levine, Chelsea Finn, Eric Mitchell, 2020, ICML.

Few-Shot Goal Inference for Visuomotor Learning and Planning

Sergey Levine, Chelsea Finn, Avi Singh, 2018, CoRL.

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

Sergey Levine, Justin Fu, Avi Singh, 2018, NeurIPS.

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

Sergey Levine, Chelsea Finn, Daniel Kappler, 2022, CoRL.

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning pdf

Sergey Levine, Honglak Lee, Ofir Nachum, 2018, ICLR.

Datasets for Data-Driven Reinforcement Learning

Justin Fu, George Tucker, Ofir Nachum, 2020 .

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets pdf

Sergey Levine, Chelsea Finn, Georgios Georgakis, 2021, Robotics: Science and Systems XVIII.

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks pdf

Sergey Levine, Eugen Solowjow, Gerrit Schoettler, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Value Iteration Networks

Pieter Abbeel, Yi Wu, Aviv Tamar, 2016, NIPS.

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors pdf

Chelsea Finn, Oleh Rybkin, Karl Pertsch, 2020, NeurIPS.

Offline Reinforcement Learning as One Big Sequence Modeling Problem pdf

Sergey Levine, Qiyang Li, Michael Janner, 2021, NeurIPS.

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

S. Levine, S. Gu, Ofir Nachum, 2021, ICML.

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning pdf

Ilya Kostrikov, Kumar Krishna Agrawal, Sergey Levine, 2018, ICLR.

MuProp: Unbiased Backpropagation for Stochastic Neural Networks pdf

Sergey Levine, Ilya Sutskever, Andriy Mnih, 2015, ICLR.

Data-Driven Offline Optimization For Architecting Hardware Accelerators

S. Levine, Aviral Kumar, Kevin Swersky, 2021, ICLR.

Sim2Real View Invariant Visual Servoing by Recurrent Control

Sergey Levine, Fereshteh Sadeghi, Alexander Toshev, 2017, ArXiv.

Sim2Real View Invariant Visual Servoing by Recurrent Control

Sergey Levine, Fereshteh Sadeghi, Alexander Toshev, 2017 .

Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation

Sergey Levine, Aurick Zhou, S. Levine, 2020, ICML.

Contrastive Learning as Goal-Conditioned Reinforcement Learning

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, NeurIPS.

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings pdf

Chelsea Finn, Dinesh Jayaraman, Sergey Levine, 2020, ICML.

Reinforcement Learning from Passive Data via Latent Intentions

S. Levine, Dibya Ghosh, Chethan Bhateja, 2023, ICML.

Imitating Past Successes can be Very Suboptimal

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, NeurIPS.

Outcome-Driven Reinforcement Learning via Variational Inference

Tim G. J. Rudner, Vitchyr H. Pong, S. Levine, 2021, NeurIPS.

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning pdf

Sergey Levine, Rishabh Agarwal, Aviral Kumar, 2020, ICLR.

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL pdf

Sergey Levine, Chelsea Finn, Aviral Kumar, 2020, NeurIPS.

DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies

Sergey Levine, Glen Berseth, Ashvin Nair, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Scalable Multi-Task Imitation Learning with Autonomous Improvement

Sergey Levine, Chelsea Finn, Daniel Kappler, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real

Mohi Khansari, Chris Harris, Alex Irpan, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift pdf

Sergey Levine, Chelsea Finn, Marvin Zhang, 2020, ArXiv.

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic pdf

Sergey Levine, Richard E. Turner, Zoubin Ghahramani, 2016, ICLR.

AMP

S. Levine, P. Abbeel, X. B. Peng, 2021, ACM Transactions on Graphics.

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction pdf

Sergey Levine, Abhishek Gupta, Aviral Kumar, 2020, NeurIPS.

Dual Generator Offline Reinforcement Learning

S. Levine, Aviral Kumar, Yevgen Chebotar, 2022, ArXiv.

Offline RL for Natural Language Generation with Implicit Language Q Learning

S. Levine, Ilya Kostrikov, Yi Su, 2022, ICLR.

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning

S. Levine, Aviral Kumar, Yevgen Chebotar, 2022, Neural Information Processing Systems.

Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting

Jianren Wang, Nicholas Rhinehart, Kris Kitani, 2020, CoRL.

Entity Abstraction in Visual Model-Based Reinforcement Learning

John D. Co-Reyes, S. Levine, J. Tenenbaum, 2019, CoRL.

Training Diffusion Models with Reinforcement Learning

Yilun Du, S. Levine, Michael Janner, 2023, ArXiv.

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

S. Levine, Michael Janner, Ilya Kostrikov, 2023, ArXiv.

Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

S. Levine, Karol Hausman, Ted Xiao, 2022, ArXiv.

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

S. Levine, Justin Fu, Mengjiao Yang, 2022, NAACL.

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

Peter R. Florence, S. Levine, Karol Hausman, 2023, ArXiv.

GNM: A General Navigation Model to Drive Any Robot

S. Levine, Dhruv Shah, N. Hirose, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models

Matthew A. Wright, S. Levine, Nicholas Rhinehart, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Pragmatic Image Compression for Human-in-the-Loop Decision-Making pdf

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2021, NeurIPS.

Temporal Difference Models: Model-Free Deep RL for Model-Based Control pdf

Sergey Levine, Shixiang Gu, Vitchyr Pong, 2018, ICLR.

Composable Deep Reinforcement Learning for Robotic Manipulation

Sergey Levine, Pieter Abbeel, Aurick Zhou, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

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

S. Levine, Chelsea Finn, Aviral Kumar, 2023, arXiv.org.

Efficient Online Reinforcement Learning with Offline Data

Philip J. Ball, S. Levine, Laura Smith, 2023, arXiv.org.

Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance

S. Levine, Vikash Kumar, Kelvin Xu, 2022, arXiv.org.

Autonomous Reinforcement Learning via Subgoal Curricula

Sergey Levine, Karol Hausman, Chelsea Finn, 2021, NeurIPS.

Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention

Sergey Levine, Kelvin Xu, Abhishek Gupta, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Learning compound multi-step controllers under unknown dynamics

Sergey Levine, Weiqiao Han, Pieter Abbeel, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control pdf

Karol Hausman, Julian Ibarz, Sergey Levine, 2020, ICLR.

OmniTact: A Multi-Directional High-Resolution Touch Sensor

Sergey Levine, Chelsea Finn, Stephen Tian, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning

S. Levine, P. Abbeel, X. B. Peng, 2023, ArXiv.

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget pdf

Yoshua Bengio, Sergey Levine, Anirudh Goyal, 2020, ICLR.

Reward-Conditioned Policies pdf

Sergey Levine, Aviral Kumar, Xue Bin Peng, 2019, ArXiv.

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

S. Levine, Alexander Toshev, Ted Xiao, 2021, ICLR.

Sequential Forecasting of 100,000 Points

Jianren Wang, Nicholas Rhinehart, Kris M. Kitani, 2020 .

Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting

Kris M. Kitani, S. Levine, Nicholas Rhinehart, 2020, ArXiv.

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios

S. Levine, G. Tucker, Justin Fu, 2022, ArXiv.

VideoFlow: A Flow-Based Generative Model for Video

Sergey Levine, Dumitru Erhan, Chelsea Finn, 2019, ArXiv.

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

S. Levine, Vikash Kumar, Chelsea Finn, 2023, Robotics: Science and Systems.

One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks pdf

Sergey Levine, Pieter Abbeel, Chelsea Finn, 2018, ArXiv.

Predictable MDP Abstraction for Unsupervised Model-Based RL

S. Levine, Seohong Park, 2023, ArXiv.

FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing

S. Levine, Dhruv Shah, Ilya Kostrikov, 2023, ArXiv.

Jump-Start Reinforcement Learning

S. Levine, Karol Hausman, Ted Xiao, 2022, ICML.

Understanding the Complexity Gains of Single-Task RL with a Curriculum

S. Levine, Yuexiang Zhai, Yi Ma, 2022, ArXiv.

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

Sergey Levine, Sehoon Ha, J. Chase Kew, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Gaurav S. Sukhatme, Sergey Levine, Stefan Schaal, 2017, ICML.

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning pdf

Sergey Levine, Aurick Zhou, Ashwin Reddy, 2021, ICML.

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

Gaurav S. Sukhatme, Karol Hausman, Ryan Julian, 2020, RSS 2020.

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

S. Levine, X. B. Peng, G. Berseth, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Reinforcement Learning from Imperfect Demonstrations pdf

Yang Gao, Sergey Levine, Trevor Darrell, 2018, ICLR.

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ArXiv.

Watch, Try, Learn: Meta-Learning from Demonstrations and Reward pdf

Sergey Levine, Chelsea Finn, Daniel Kappler, 2019, ICLR.

Understanding the World Through Action

Sergey Levine, S. Levine, 2021, CoRL.

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Richard E. Turner, Zoubin Ghahramani, Bernhard Scholkopf, 2017, NIPS.

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

Amrith Rajagopal Setlur, Annie S. Chen, S. Levine, 2023, ArXiv.

The False Promise of Imitating Proprietary LLMs

S. Levine, P. Abbeel, Xinyang Geng, 2023, ArXiv.

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

Amrith Rajagopal Setlur, S. Levine, Chelsea Finn, 2023, ICLR.

Adversarial Policies Beat Superhuman Go AIs

S. Levine, A. Gleave, Michael Dennis, 2022, 2211.00241.

Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks

S. Levine, Ashvin Nair, Kuan Fang, 2022, CoRL.

Morphology-Agnostic Visual Robotic Control

Alexei A. Efros, Glen Berseth, Brian Yang, 2019, IEEE Robotics and Automation Letters.

3D Neural Scene Representations for Visuomotor Control

S. Levine, S. Dasari, A. Xie, 2021 .

The Mirage of Action-Dependent Baselines in Reinforcement Learning pdf

Sergey Levine, Richard E. Turner, Zoubin Ghahramani, 2018, ICML.

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

S. Levine, Ashvin Nair, Kuan Fang, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2023, ArXiv.

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

Sergey Levine, Ruslan Salakhutdinov, Tianjun Zhang, 2021, ICLR.

Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

S. Levine, Karol Hausman, Ted Xiao, 2022, Robotics: Science and Systems.

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning pdf

Sergey Levine, Michael I. Jordan, Ion Stoica, 2018, ArXiv.

Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

S. Levine, Rishabh Agarwal, G. Tucker, 2022, ICLR.

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning

S. Levine, Karol Hausman, Chelsea Finn, 2020, CoRL.

The Information Geometry of Unsupervised Reinforcement Learning pdf

Sergey Levine, Ruslan Salakhutdinov, Benjamin Eysenbach, 2021, ICLR.

Training on Test Data with Bayesian Adaptation for Covariate Shift pdf

Sergey Levine, Aurick Zhou, S. Levine, 2021, ArXiv.

Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior

Anca D. Dragan, Sergey Levine, Siddharth Reddy, 2018, NeurIPS.

Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation pdf

Sergey Levine, Justin Fu, S. Levine, 2021, ICLR.

Learning and Adapting Agile Locomotion Skills by Transferring Experience

S. Levine, Laura Smith, Sehoon Ha, 2023, Robotics: Science and Systems.

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

S. Levine, F. Ebert, Chelsea Finn, 2021, Robotics: Science and Systems.

Trust Region Policy Optimization

Michael I. Jordan, J. Schulman, Philipp Moritz, 2015, ICML.

Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection

Sergey Levine, Peter Pastor, Alex Krizhevsky, 2016, ISER.

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization pdf

Sergey Levine, Aaron C. Courville, Rishabh Agarwal, 2021, ICLR.

Confidence-Conditioned Value Functions for Offline Reinforcement Learning

S. Levine, Aviral Kumar, Joey Hong, 2022, ICLR.

Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints

S. Levine, Aviral Kumar, Yevgen Chebotar, 2022, ArXiv.

Data Sharing without Rewards in Multi-Task Ofﬂine Reinforcement Learning

Dario Amodei, S. Levine, K. Kavukcuoglu, 2022 .

Efficient Online Reinforcement Learning with Offline Data

Philip J. Ball, S. Levine, Laura Smith, 2023, ICML.

Multi-Stage Cable Routing through Hierarchical Imitation Learning

S. Levine, S. Schaal, Jianlan Luo, 2023, ArXiv.

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

S. Levine, Chelsea Finn, Aviral Kumar, 2023, ArXiv.

Reinforcement Learning with O line Data

Philip J. Ball, S. Levine, Laura Smith, 2023 .

Understanding the Complexity Gains of Single-Task RL with a Curriculum

S. Levine, Yuexiang Zhai, Yi Ma, 2022, ArXiv.

Unifying Map and Landmark Based Representations for Visual Navigation pdf

Jitendra Malik, Sergey Levine, Saurabh Gupta, 2017, ArXiv.

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

Amrith Rajagopal Setlur, Annie S. Chen, S. Levine, 2023, ArXiv.

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2021, NeurIPS.

Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control

S. Levine, A. Dragan, A. Kolobov, 2023, arXiv.org.

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2022, ICLR.

Ignorance is Bliss: Robust Control via Information Gating

S. Levine, Riashat Islam, Philip Bachman, 2023, ArXiv.

Automatically Composing Representation Transformations as a Means for Generalization

S. Levine, Abhishek Gupta, T. Griffiths, 2018, International Conference on Learning Representations.

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios

S. Levine, G. Tucker, Justin Fu, 2022, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control

S. Levine, A. Dragan, A. Kolobov, 2023, ArXiv.

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

S. Levine, Vincent Vanhoucke, M. Ryoo, 2022, Robotics: Science and Systems.

EARNING WITH P OLICY S KETCHES

S. Levine, D. Klein, Jacob Andreas, 2016 .

Learning to Walk in the Real World with Minimal Human Effort

S. Levine, Sehoon Ha, P. Xu, 2020, CoRL.

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

S. Levine, Clare Lyle, Angelos Filos, 2021, ICML.

Learning Robotic Manipulation of Granular Media

Sergey Levine, Jonathan Tompson, Connor Schenck, 2017, CoRL.

HIQL: Offline Goal-Conditioned RL with Latent States as Actions

S. Levine, Benjamin Eysenbach, Dibya Ghosh, 2023, ArXiv.

Predictable MDP Abstraction for Unsupervised Model-Based RL

S. Levine, Seohong Park, 2023, ICML.

Dynamics-Aware Unsupervised Skill Discovery

Karol Hausman, Sergey Levine, Vikash Kumar, 2020, ICLR 2020.

Artificial Intelligence for Prosthetics - challenge solutions pdf

N. E. Toklu, Wojciech Jaskowski, Jeremy D. Watson, 2019, The NeurIPS '18 Competition.

Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play pdf

Risto Miikkulainen, Sergey Levine, Navdeep Jaitly, 2018, ArXiv.

Improving Generalization with Approximate Factored Value Functions

S. Levine, Amy Zhang, Shagun Sodhani, 2023, Trans. Mach. Learn. Res..

SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning pdf

Sergey Levine, Pieter Abbeel, Matthew J. Johnson, 2018, ArXiv.

Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states

Sergey Levine, Anurag Ajay, Pieter Abbeel, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Guided Policy Search via Approximate Mirror Descent

Sergey Levine, William H. Montgomery, S. Levine, 2016, NIPS.

A Connection between One-Step RL and Critic Regularization in Reinforcement Learning

S. Levine, R. Salakhutdinov, Benjamin Eysenbach, 2023, ICML.

Learning to Influence Human Behavior with Offline Reinforcement Learning

S. Levine, A. Dragan, Joey Hong, 2023 .

Dynamical Distance Learning for Unsupervised and Semi-Supervised Skill Discovery pdf

Sergey Levine, Xinyang Geng, Tuomas Haarnoja, 2019, ArXiv.

Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty

Matthew A. Wright, S. Levine, Nicholas Rhinehart, 2022, Conference on Robot Learning.

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Peter R. Florence, Montse Gonzalez Arenas, S. Levine, 2023, ArXiv.

SACSoN: Scalable Autonomous Control for Social Navigation

S. Levine, Dhruv Shah, N. Hirose, 2023, 2306.01874.

SACSoN: Scalable Autonomous Data Collection for Social Navigation

S. Levine, Dhruv Shah, N. Hirose, 2023, ArXiv.

ViNT: A Foundation Model for Visual Navigation

S. Levine, Dhruv Shah, N. Hirose, 2023, ArXiv.

Efficient Deep Reinforcement Learning Requires Regulating Overfitting

S. Levine, Aviral Kumar, Ilya Kostrikov, 2023, ICLR.

Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning pdf

Sergey Levine, Jongwook Choi, Shixiang Shane Gu, 2021, ArXiv.

Assisted Perception: Optimizing Observations to Communicate State

S. Levine, A. Dragan, S. Reddy, 2020, CoRL.

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

S. Levine, Chelsea Finn, Aviral Kumar, 2023, ArXiv.

Confidence-Conditioned Value Functions for Offline Reinforcement Learning

S. Levine, Aviral Kumar, Joey Hong, 2022, ICLR.

Contrastive Example-Based Control

S. Levine, R. Salakhutdinov, Chelsea Finn, 2023, L4DC.

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

Montse Gonzalez Arenas, S. Levine, P. Pastor, 2023, Robotics: Science and Systems.

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

S. Levine, F. Ebert, Chelsea Finn, 2022, Robotics: Science and Systems.

HIQL: Offline Goal-Conditioned RL with Latent States as Actions

S. Levine, Benjamin Eysenbach, Dibya Ghosh, 2023, ArXiv.

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions

S. Levine, Karol Hausman, Ofir Nachum, 2023, ArXiv.

MOPO: Model-based Offline Policy Optimization

James Y. Zou, S. Levine, Chelsea Finn, 2020, NeurIPS.

Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations

S. Levine, Joey Hong, Anca Dragan, 2023, ArXiv.

Ecological Reinforcement Learning pdf

Sergey Levine, Glen Berseth, John D. Co-Reyes, 2020, ArXiv.

Guided Policy Search as Approximate Mirror Descent

S. Levine, William H. Montgomery, 2016, NIPS 2016.

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

Kumar Krishna Agrawal, S. Levine, Jonathan Tompson, 2018, ICLR.

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

S. Levine, S. Schaal, Karol Hausman, 2017, ICML.

Learning Dynamic Manipulation Skills under Unknown Dynamics with Guided Policy Search

S. Levine, P. Abbeel, 2014 .

Training Diffusion Models with Reinforcement Learning

S. Levine, Michael Janner, Yilun Du, 2023, ArXiv.

TRASS: Time Reversal as Self-Supervision

Sergey Levine, Chelsea Finn, Mohammad Babaeizadeh, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

S. Levine, D. Erhan, Chelsea Finn, 2019, ICLR.

The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

S. Levine, E. Adelson, R. Calandra, 2017, CoRL.

SACSoN: Scalable Autonomous Control for Social Navigation

S. Levine, Dhruv Shah, N. Hirose, 2023, IEEE Robotics and Automation Letters.

ViNT: A Foundation Model for Visual Navigation

S. Levine, Dhruv Shah, N. Hirose, 2023, CoRL.

Learning Invariant Representations for Reinforcement Learning without Reconstruction pdf

Rowan McAllister, Yarin Gal, Roberto Calandra, 2020, ICLR.

Bayesian Adaptation for Covariate Shift

S. Levine, Aurick Zhou, 2021, NeurIPS.