Abhishek Gupta
Assistant Professor
Develops reinforcement learning and robot learning algorithms for robots that learn continually in the real world.
Abhishek Gupta's lab at UW works on manipulation & robot learning and reinforcement learning. 44 papers since 2023, 43 at top venues, mostly ICLR and ICRA. 10 PhD students. 1 alumnus. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 98%
- PhD students
- 10
- MS students
- 0
- alumni listed
- 1
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 10 papers2025 · T1 9
- 19 papers2024 · T1 19
- 15 papers2023 · T1 15
T1 venuesOtherVenues: ICLR, ICRA, NeurIPS, CoRL, RSS
Research focuspaper titles that use each topic's words
- Manipulation & robot learning31 papers
- Reinforcement learning23 papers
- real7 papers
- develops reinforcement0 papers
- learning algorithms0 papers
Paper list
From the lab homepage.
202510 papers
- CoRL (T1)ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning (opens in a new tab)
- CVPR (T1)DRAWER: Digital Reconstruction and Articulation With Environment Realism (opens in a new tab)
- ICRA (T1)DUOLINGO: Dynamics Utilization for Online Translation of Actions
- ICLR (T1)HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation (opens in a new tab)
- ICLR (T1)Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning (opens in a new tab)
- CoRL (T1)RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies (opens in a new tab)
- ICLR (T1)SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks (opens in a new tab)
- CoRL (T1)Steering Your Diffusion Policy with Latent Space Reinforcement Learning (opens in a new tab)
- ICLR (T1)STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning (opens in a new tab)
- RSS (Best Paper Award), Best Paper at ICML Workshop on Building Physically Plausible World ModelsUnified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets (opens in a new tab)
202419 papers
- ICLR (T1)ASID: Active Exploration for System Identification and Reconstruction in Robotic Manipulation (opens in a new tab)
- ICLR (T1)CCIL: Continuity-Based Data Augmentation for Corrective Imitation Learning (opens in a new tab)
- IROS (T1)Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels (opens in a new tab)
- NeurIPS (T1)Distributional Successor Features Enable Zero-Shot Policy Optimization (opens in a new tab)
- RSS (T1)DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset (opens in a new tab)
- ICLR (T1)Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning (opens in a new tab)
- NeurIPS (T1)Learning to Cooperate with Humans using Generative Agents (opens in a new tab)
- ICRA (T1)Learning to Grasp in Clutter with Interactive Visual Failure Prediction
- ICRA (T1)Lifelong Robot Learning with Human Assisted Language Planners (opens in a new tab)
- ICLR (T1)Modeling Boundedly Rational Agents with Latent Inference Budgets (opens in a new tab)
- NeurIPS (T1)Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL (opens in a new tab)
- NeurIPS (T1)Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning (opens in a new tab)
202315 papers
- CoRL (T1)Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback (opens in a new tab)
- NeurIPS (T1)Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets (opens in a new tab)
- NeurIPS (T1)Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback (opens in a new tab)
- RSS (T1)Cherry-picking with reinforcement learning (opens in a new tab)
- ICRA (T1)Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning (opens in a new tab)
- ICRA (T1)Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance (opens in a new tab)
- CoRL (T1)Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching (opens in a new tab)
- RSS (T1)GenAug: Retargeting behaviors to unseen situations via Generative Augmentation (opens in a new tab)
- ICML (T1)Guiding Pretraining in Reinforcement Learning with Large Language Models (opens in a new tab)
- ICLR (T1)Learning to Extrapolate: A Transductive Approach (opens in a new tab)
- CoRL (T1)REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation (opens in a new tab)
- NeurIPS (T1)RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability (opens in a new tab)
Where alumni went1 listed, 0% to industry
Industry 0Academia 1
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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