World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
6079
World Ranking
9368
National Ranking
372

Overview

Animesh Garg is affiliated with the University of Toronto in Canada. Their research spans multiple areas within computer science and engineering, with a particular focus on artificial intelligence and robotics.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these fields, they specialize in several subfields, notably:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Biomedical Engineering
  • Mechanical Engineering

The major topics their work addresses encompass:

  • Reinforcement Learning in Robotics
  • Robot Manipulation and Learning
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Modular Robots and Swarm Intelligence
  • Robotic Locomotion and Control

They have contributed extensively to academic publications, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Autonomous Robots
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Recent significant papers authored or co-authored include:

  • "X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments," 2023, IEEE Robotics and Automation Letters
  • "Large language models for chemistry robotics," 2023, Autonomous Robots
  • "Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation," 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • "ORGANA: A robotic assistant for automated chemistry experimentation and characterization," 2024, Matter

The scientist has collaborated frequently with several individuals, including:

  • Florian Shkurti
  • Dieter Fox
  • Samarth Sinha
  • Alán Aspuru-Guzik
  • Homanga Bharadhwaj

Best Publications

  • ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

    Unknown

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

    Michelle A. Lee;Yuke Zhu;Krishnan Srinivasan;Parth Shah

  • Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning:

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

    Adithyavairavan Murali;Siddarth Sen;Ben Kehoe;Animesh Garg

  • Learning task-oriented grasping for tool manipulation from simulated self-supervision:

    Kuan Fang;Yuke Zhu;Animesh Garg;Animesh Garg;Andrey Kurenkov

  • Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

    Siddarth Sen;Animesh Garg;David V. Gealy;Stephen McKinley

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

    Michelle A. Lee;Yuke Zhu;Peter Zachares;Matthew Tan

  • Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

    Danfei Xu;Suraj Nair;Yuke Zhu;Julian Gao

  • X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

    Unknown

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

    Brijen Thananjeyan;Animesh Garg;Sanjay Krishnan;Carolyn Chen

  • Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

    Roberto Martin-Martin;Michelle A. Lee;Rachel Gardner;Silvio Savarese

  • Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

    De-An Huang;Suraj Nair;Danfei Xu;Yuke Zhu

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

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Li Fei-Fei

  • DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

    Andrey Kurenkov;Jingwei Ji;Animesh Garg;Viraj Mehta

  • ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Jonathan Booher

  • Weakly Supervised 3D Reconstruction with Adversarial Constraint

    JunYoung Gwak;Christopher B. Choy;Manmohan Chandraker;Animesh Garg

  • Transition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning.

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

    Unknown

  • Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

    De-An Huang;Shyamal Buch;Lucio Dery;Animesh Garg

  • A single-use haptic palpation probe for locating subcutaneous blood vessels in robot-assisted minimally invasive surgery

    Stephen McKinley;Animesh Garg;Siddarth Sen;Rishi Kapadia

  • TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning

    Adithyavairavan Murali;Animesh Garg;Sanjay Krishnan;Florian T. Pokorny

  • Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

    Animesh Garg;Siddarth Sen;Rishi Kapadia;Yiming Jen

  • Large language models for chemistry robotics

    Unknown

  • IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

    Ajay Mandlekar;Fabio Ramos;Byron Boots;Silvio Savarese

  • Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

    Michael Danielczuk;Andrey Kurenkov;Ashwin Balakrishna;Matthew Matl

  • DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

    Eric Heiden;Miles Macklin;Yashraj S Narang;Dieter Fox

  • Transition state clustering

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Causal Discovery in Physical Systems from Videos

    Yunzhu Li;Antonio Torralba;Animashree Anandkumar;Dieter Fox

Frequent Co-Authors

Silvio Savarese
Silvio Savarese Stanford University
Ken Goldberg
Ken Goldberg University of California, Berkeley
Yuke Zhu
Yuke Zhu The University of Texas at Austin
Li Fei-Fei
Li Fei-Fei Stanford University
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Sachin Patil
Sachin Patil University of California, Berkeley
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Jeannette Bohg
Jeannette Bohg Stanford University
Hugo Larochelle
Hugo Larochelle Google (United States)
Byron Boots
Byron Boots University of Washington

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