Garg, Animesh

ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA)

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 613

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., Garg, A., & Bohg, J. (2019) · 2019 International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 349

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

Thananjeyan, B., Garg, A., Krishnan, S., Chen, C., Miller, L., & Goldberg, K. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA)

Surgical Simulation and TrainingReinforcement Learning in Robotics

Cited by 144

ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

Mandlekar, A., Zhu, Y., Garg, A., Booher, J., Spero, M., Tung, A., Gao, J., Emmons, J., Gupta, A., Orbay, E., et al. (2018) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 134

ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization

Darvish, K., Skreta, M., Zhao, Y., Yoshikawa, N., Som, S., Bogdanovic, M., Cao, Y., Hao, H., Xu, H., Aspuru-Guzik, A., et al. (2024) · arXiv

Various Chemistry Research TopicsRobotics and Automated Systems

Cited by 133

Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

Mandlekar, A., Zhu, Y., Garg, A., Fei-Fei, L., & Savarese, S. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Adversarial Robustness in Machine LearningReinforcement Learning in Robotics

Cited by 116

Large language models for chemistry robotics

Yoshikawa, N., Skreta, M., Darvish, K., Arellano-Rubach, S., Ji, Z., Bjørn Kristensen, L., Li, A. Z., Zhao, Y., Xu, H., Kuramshin, A., et al. (2023) · Autonomous Robots

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 112

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F. (2021) · Robotics: Science and Systems XVII

Soft Robotics and ApplicationsRobot Manipulation and Learning

Cited by 66

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity

Mandlekar, A., Booher, J., Spero, M., Tung, A., Gupta, A., Zhu, Y., Garg, A., Savarese, S., & Fei-Fei, L. (2019) · arXiv

Robot Manipulation and LearningTeleoperation and Haptic Systems

Cited by 48

Counterfactual Data Augmentation using Locally Factored Dynamics

Pitis, S., Creager, E., & Garg, A. (2020) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 26

ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 21

Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting

Skreta, M., Yoshikawa, N., Arellano-Rubach, S., Ji, Z., Kristensen, L. B., Darvish, K., Aspuru-Guzik, A., Shkurti, F., & Garg, A. (2023) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 18

A large-scale randomized study of large language model feedback in peer review

Thakkar, N., Yuksekgonul, M., Silberg, J., Garg, A., Peng, N., Sha, F., Yu, R., Vondrick, C., & Zou, J. (2026) · Nature Machine Intelligence

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 11

MoCoDA: Model-based Counterfactual Data Augmentation

Pitis, S., Creager, E., Mandlekar, A., & Garg, A. (2022) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 8

S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Sinha, S., Mandlekar, A., & Garg, A. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 5

Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025

Thakkar, N., Yuksekgonul, M., Silberg, J., Garg, A., Peng, N., Sha, F., Yu, R., Vondrick, C., & Zou, J. (2025) · arXiv

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 4

PWM: Policy learning with multi-task world models

Georgiev, I., Giridhar, V., Hansen, N., & Garg, A. (2024) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 0