Abhishek Gupta

I am an assistant professor in computer science and engineering at the Paul G. Allen School at the University of Washington. I lead the Washington Embodied Intelligence and Robotics Development (WEIRD) lab.

Previously, I was a post-doctoral scholar at MIT, collaborating with Russ Tedrake and Pulkit Agarwal.

I spent 6 wonderful years completing my PhD in machine learning and robotics at BAIR at UC Berkeley, where I was advised by Professor Sergey Levine and Professor Pieter Abbeel. Previously, I completed my bachelors degree also at UC Berkeley.

My main research goal is to develop algorithms which enable robotic systems to learn how to perform complex tasks in a variety of unstructured environments like offices and homes. To that end, I work towards building deep reinforcement learning algorithms that can learn in the real world, with and around humans. Recently our work has focused on deployment time reinforcement learning, learning on deployment directly in human-centric environments under the following themes

Learning foundation models from off-domain sources of data such as video, simulation or generative models
Fast and efficient real world adaptation using pre-trained priors
Human in the loop interaction and adaptation
Real-to-sim-to-real policy learning methods

More generally, I have been interested in the problems of building scalable foundation models from off-domain data, fast and safe adaptation with RL, human in the loop reinforcement learning, reward specification, continual real world data collection and learning, offline reinforcement learning for robotics, multi-task and meta-learning and dexterous manipulation with robotic hands and studying generalization and extrapolation for policies and models. I am also excited about a broader space of problems including algorithms for assistive robotics, safe exploration, robustness and compositionality in deep learning, and all things embodied intelligence.

For prospective students: I am looking for highly motivated Ph.D students and postdoctoral researchers to join our group. For Ph.D. students, I highly encourage you to apply to the UW CSE Ph.D program through the Allen school, and list me as an advisor of interest. I am very open to coadvising requests as well, please mention this in your application. I ask that you do not email me directly with regard to PhD admissions until after you are admitted, as I will not be able to reply to emails from individual applicants. Rest assured I will give your application a read! For postdoctoral scholar applications, please send me an email with your CV and a statement of your interests.

Email / CV / GitHub / Google Scholar / Ph.D. Thesis

Workshop Papers, Submissions and Pre-prints

	Ecological Reinforcement Learning John D Co-Reyes, Suvansh Sanjeev, Glen Berseth, Abhishek Gupta, Sergey Levine arXiv Preprint paper
	Unsupervised meta-learning for reinforcement learning Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine arXiv preprint, best paper at LLARLA workshop at ICML 2018 paper / blog
	Accelerating online reinforcement learning with offline datasets Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine arXiv preprint paper
	Learning latent state representation for speeding up exploration Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel arXiv preprint paper

Publications

	2024
	Teaching Robots with Show and Tell: Using Foundation Models to Synthesize Robot Policies from Language and Visual Demonstration Michael Murray, Abhishek Gupta, Maya Cakmak CoRL 2024 paper
	Learning to Cooperate with Humans using Generative Agents Yancheng Liang, Daphne Chen, Abhishek Gupta, Simon S. Du, Natasha Jaques NeurIPS 2024 paper
	Distributional Successor Features Enable Zero-Shot Policy Optimization Chuning Zhu, Xinqi Wang, Tyler Han, Simon S. Du, Abhishek Gupta NeurIPS 2024 paper
	Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, Natasha Jaques NeurIPS 2024 (Spotlight) paper
	Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Byron Boots, Kevin Jamieson, Abhishek Gupta NeurIPS 2024 paper
	DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset DROID Collaboration Team RSS 2024 paper
	Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, Pulkit Agrawal RSS 2024 paper
	URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images Zoey Chen, Aaron Walsman, Marius Memmel, Kaichun Mo, Alex Fang, Karthikeya Vemuri, Alan Wu, Dieter Fox, Abhishek Gupta RSS 2024 paper
	Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels Abhay Deshpande, Liyiming Ke, Quinn Pfeifer, Abhishek Gupta, Siddhartha S. Srinivasa IROS 2024 paper
	CCIL: Continuity-Based Data Augmentation for Corrective Imitation Learning Liyiming Ke, Yunchu Zhang, Abhay Deshpande, Siddhartha Srinivasa, Abhishek Gupta ICLR 2024 paper
	Modeling Boundedly Rational Agents with Latent Inference Budgets Athul Paul Jacob, Abhishek Gupta, Jacob Andreas ICLR 2024 paper
	Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning Zhaoyi Zhou, Chuning Zhu, Runlong Zhou, Qiwen Cui, Abhishek Gupta, Simon Shaolei Du ICLR 2024 paper
	ASID: Active Exploration for System Identification and Reconstruction in Robotic Manipulation Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta ICLR 2024 paper
	Learning to Grasp in Clutter with Interactive Visual Failure Prediction Michael Murray, Abhishek Gupta, Maya Cakmak ICRA 2024
	Lifelong Robot Learning with Human Assisted Language Planners Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, Luca Weihs ICRA 2024 paper
	Rank2Reward: Learning Shaped Reward Functions from Passive Video Daniel Yang, Davin Tjia, Jacob Berg, Dima Damen, Pulkit Agrawal, Abhishek Gupta ICRA 2024 paper
	Universal Visual Decomposer: Long-Horizon Manipulation Made Easy Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, Luca Weihs ICRA 2024 paper
	SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning Jianlan Luo, Zheyuan Hu, Charles Xu, You Liang Tan, Jacob Berg, Archit Sharma, Stefan Schaal, Chelsea Finn, Abhishek Gupta, Sergey Levine ICRA 2024 paper
	2023
	Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback Max Balsells I Pamies, Marcel Torne Villasevil, Zihan Wang, Samedh Desai, Pulkit Agrawal, Abhishek Gupta CoRL 2023 paper
	Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching HJ Suh, Glen Chou, Hongkai Dai, Lujie Yang, Abhishek Gupta, Russ Tedrake CoRL 2023 paper
	REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, Sergey Levine CoRL 2023 paper
	Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal NeurIPS 2023 paper
	Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback Marcel Torne, Max Balsells, Zihan Wang, Samedh Desai, Tao Chen, Pulkit Agrawal, Abhishek Gupta NeurIPS 2023 paper
	RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta NeurIPS 2023 (Spotlight) paper
	Self-Supervised Reinforcement Learning that Transfers using Random Features Boyuan Chen, Chuning Zhu, Pulkit Agrawal, Kaiqing Zhang, Abhishek Gupta NeurIPS 2023 paper
	Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective Max Simchowitz, Abhishek Gupta, Kaiqing Zhang COLT 2023 paper
	Guiding Pretraining in Reinforcement Learning with Large Language Models Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas ICML 2023 paper
	GenAug: Retargeting behaviors to unseen situations via Generative Augmentation Zoey Chen, Sho Kiami, Abhishek Gupta, Vikash Kumar RSS 2023 (Best Systems Paper Finalist) paper
	Cherry-picking with reinforcement learning Yunchu Zhang, Liyiming Ke, Abhay Deshpande, Abhishek Gupta, Siddhartha Srinivasa RSS 2023 paper
	Learning to Extrapolate: A Transductive Approach Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal ICLR 2023 paper
	TactoFind: A Tactile Only System for Object Retrieval Sameer Pai, Tao Chen, Megha Tippur, Edward Adelson, Abhishek Gupta, Pulkit Agrawal ICRA 2023 paper
	Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning Abhishek Gupta, Corey Lynch, Brandon Kinman, Garrett Peake, Sergey Levine, Karol Hausman ICRA 2023 paper
	Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance Kelvin Xu, Zheyuan Hu, Ria Doshi, Aaron Rovinsky, Vikash Kumar, Abhishek Gupta, Sergey Levine ICRA 2023 paper
	2022
	Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation iuyu Chen, Karl Van Wyk, Yu-Wei Chao, Wei Yang, Arsalan Mousavian, Abhishek Gupta, Dieter Fox CoRL 2022 paper
	Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity Abhishek Gupta, Aldo Pacchiano, Simon Zhai, Sham Kakade, Sergey Levine NeurIPS 2022 paper
	Distributionally Adaptive Meta Reinforcement Learning Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal NeurIPS 2022 paper
	Autonomous Reinforcement Learning: Formalism and Benchmarking Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn ICLR 2022 paper
	2021
	Teachable Reinforcement Learning via Advice Distillation Olivia Watkins, Trevor Darrell, Pieter Abbeel, Jacob Andreas, Abhishek Gupta NeurIPS 2021 paper
	Persistent Reinforcement Learning via Subgoal Curricula Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn NeurIPS 2021 paper
	Adaptive risk minimization: A meta-learning approach for tackling group shift Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn NeurIPS 2021 paper / blog
	Which Mutual-Information Representation Learning Objectives are Sufficient for Control? Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine NeurIPS 2021 paper
	Fully Autonomous Real-World Reinforcement Learning for Mobile Manipulation Charles Sun, Jedrzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine CoRL 2021 paper
	Learning to reach goals via iterated supervised learning Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, Sergey Levine ICLR 2021 (Oral) paper / blog
	MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning Kevin Li, Abhishek Gupta, Ashwin D Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine ICML 2021 paper / website
	Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine ICRA 2021 paper / website
	2020
	The ingredients of real-world robotic reinforcement learning Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine ICLR 2020 (spotlight)* paper / blog
	Discor: Corrective feedback in reinforcement learning via distribution correction Aviral Kumar, Abhishek Gupta, Sergey Levine NeurIPS 2020 (spotlight) paper / blo
	Gradient surgery for multi-task learning Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn NeurIPS 2020 paper
	2019
	Unsupervised curricula for visual meta-reinforcement learning Allan Jabri, Kyle Hsu, Benjamin Eysenbach, Abhishek Gupta, Alexei Efros, Sergey Levine, Chelsea Finn NeurIPS 2019 (spotlight) paper
	ROBEL: RObotics BEnchmarks for Learning with low-cost robots Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar CoRL 2019 paper / blog
	Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman CORL 2019 paper / website
	Guided meta-policy search Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn NeurIPS 2019 (spotlight) paper
	Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar ICRA 2019 paper / blog
	Diversity is all you need: Learning skills without a reward function Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine ICLR 2019 paper / video
	Guiding policies with language via meta-learning John D Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, John DeNero, Pieter Abbeel, Sergey Levine ICLR 2019 paper
	Learning actionable representations with goal-conditioned policies Dibya Ghosh, Abhishek Gupta, Sergey Levine ICLR 2019 paper
	Automatically composing representation transformations as a means for generalization Michael B. Chang, Abhishek Gupta, Sergey Levine, Thomas Griffith ICLR 2019 paper
	2018
	Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings John D Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine ICML 2018* paper
	Imitation from observation: Learning to imitate behaviors from raw video via context translation YuXuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine ICRA 2018 paper / video
	Meta-reinforcement learning of structured exploration strategies Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, Sergey Levine NeurIPS 2018 (spotlight) paper / code
	Learning complex dexterous manipulation with deep reinforcement learning and demonstrations Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzanni, John Schulman, Emanuel Todorov, Sergey Levine RSS 2018 paper / video
	2017
	Learning modular neural network policies for multi-task and multi-robot transfer Abhishek Gupta, Coline Devin, Trevor Darrell, Pieter Abbeel, Sergey Levine ICRA 2017 paper / video
	Learning invariant feature spaces to transfer skills with reinforcement learning Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine ICLR 2017 paper / video
	2016
	Learning dexterous manipulation for a soft robotic hand from human demonstrations Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel IROS 2016 paper / video
	Guided search for task and motion plans using learned heuristics Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddhart Srivastava, Edward Groshev, Christopher Lin, Pieter Abbeel ICRA 2016 paper / video
	2015
	Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation Alex Lee, Abhishek Gupta, Henry Lu, Sergey Levine, Pieter Abbeel IROS 2015 paper
	Learning force-based manipulation of deformable objects from multiple demonstrations Alex X. Lee, Henry Lu, Abhishek Gupta, Sergey Levine, Pieter Abbeel ICRA 2015 paper
	Tractability of planning with loops Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell AAAI 2015 paper / video

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Last updated January 2021.