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, I have been specifically focusing on the problems of human in the loop reinforcement learning, reward specification, continual real world data collection and learning, fast adaptation with meta learning for robotics, 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

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, C├ędric 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

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

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

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

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

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

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

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

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

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

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

Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine
ICLR 2019
paper / video

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

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

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

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.