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D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
11612
World Ranking
5693
National Ranking
1588

Overview

Igor Mordatch is a researcher affiliated with Google in the United States, active primarily in the field of computer science. Their work spans several subfields including artificial intelligence, computer vision and pattern recognition, control and systems engineering, structural biology, and surfaces, coatings and films. The primary concentration of their research lies in artificial intelligence.

Their main topics of investigation cover a range of areas including reinforcement learning in robotics, topic modeling, multimodal machine learning applications, natural language processing techniques, robot manipulation and learning, domain adaptation and few-shot learning, as well as advanced electron microscopy techniques and applications.

Mordatch has contributed a significant number of publications, with 48 appearing in arXiv (Cornell University), along with several papers in venues such as Microscopy and Microanalysis, Proceedings of the AAAI Conference on Artificial Intelligence, Lirias (KU Leuven), and Advanced Materials Interfaces.

Some notable recent papers include:

  • Decision Transformer: Reinforcement Learning via Sequence Modeling (2021, arXiv (Cornell University))
  • PaLM-E: An Embodied Multimodal Language Model (2023, arXiv (Cornell University))
  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023, arXiv (Cornell University))
  • Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, arXiv (Cornell University))
  • Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents (2022, arXiv (Cornell University))

Frequent collaborators include Pieter Abbeel, Sergey Levine, Yilun Du, Ekin D. Cubuk, and Brian Ichter, reflecting ongoing partnerships within these areas of study.

Best Publications

  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

    Ryan Lowe;Yi Wu;Aviv Tamar;Jean Harb

  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

    Ryan Lowe;Yi Wu;Aviv Tamar;Jean Harb

  • PaLM-E: An Embodied Multimodal Language Model

    Unknown

  • Emergence of Grounded Compositional Language in Multi-Agent Populations

    Igor Mordatch;Pieter Abbeel

  • Inner Monologue: Embodied Reasoning through Planning with Language Models

    Unknown

  • Discovery of complex behaviors through contact-invariant optimization

    Igor Mordatch;Emanuel Todorov;Zoran Popović

  • Decision Transformer: Reinforcement Learning via Sequence Modeling

    Lili Chen;Kevin Lu;Aravind Rajeswaran;Kimin Lee

  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    Unknown

  • Emergent Tool Use From Multi-Agent Autocurricula

    Bowen Baker;Ingmar Kanitscheider;Todor Markov;Yi Wu

  • Learning with Opponent-Learning Awareness

    Jakob Foerster;Richard Y. Chen;Maruan Al-Shedivat;Shimon Whiteson

  • RT-1: Robotics Transformer for Real-World Control at Scale

    Unknown

  • Feature-based locomotion controllers

    Martin de Lasa;Igor Mordatch;Aaron Hertzmann

  • Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

    Maruan Al-Shedivat;Trapit Bansal;Yuri Burda;Ilya Sutskever

  • Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model

    Paul F. Christiano;Zain Shah;Igor Mordatch;Jonas Schneider

  • Robust physics-based locomotion using low-dimensional planning

    Igor Mordatch;Martin de Lasa;Aaron Hertzmann

  • Navigation system for a 3d virtual scene

    George Fitzmaurice;Justin Matejka;Igor Mordatch;Gord Kurtenbach

  • Emergent Complexity via Multi-Agent Competition

    Trapit Bansal;Jakub Pachocki;Szymon Sidor;Ilya Sutskever

  • Implicit Generation and Generalization in Energy-Based Models.

    Yilun Du;Igor Mordatch

  • Implicit Generation and Modeling with Energy Based Models

    Yilun Du;Igor Mordatch

  • Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

    Kendall Lowrey;Aravind Rajeswaran;Sham M. Kakade;Emanuel Todorov

  • Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

    Igor Mordatch;Kendall Lowrey;Emanuel Todorov

  • Contact-invariant optimization for hand manipulation

    Igor Mordatch;Zoran Popović;Emanuel Todorov

  • Combining the benefits of function approximation and trajectory optimization.

    Igor Mordatch;Emo Todorov

  • Rearrangement: A Challenge for Embodied AI.

    Dhruv Batra;Angel X. Chang;Sonia Chernova;Andrew J. Davison

  • Interactive control of diverse complex characters with neural networks

    Igor Mordatch;Kendall Lowrey;Galen Andrew;Zoran Popovic

  • Multiscale 3D navigation

    James McCrae;Igor Mordatch;Michael Glueck;Azam Khan

  • Three-dimensional orientation indicator and controller

    Anirban Ghosh;Igor Mordatch;Azam Khan;George William Fitzmaurice

  • Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

    Cathy Wu;Aravind Rajeswaran;Yan Duan;Vikash Kumar

  • Stylizing animation by example

    Pierre Bénard;Forrester Cole;Michael Kass;Igor Mordatch

Frequent Co-Authors

Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Emanuel Todorov
Emanuel Todorov University of Washington
Azam Khan
Azam Khan Trax.co
Sergey Levine
Sergey Levine University of California, Berkeley
Vikash Kumar
Vikash Kumar University of Washington
George Fitzmaurice
George Fitzmaurice Autodesk (United States)
Aviv Tamar
Aviv Tamar Technion – Israel Institute of Technology

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