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Computer Science

D-Index
33
Citations
23959
World Ranking
12337
National Ranking
4991

Overview

Yuval Tassa is a researcher affiliated with Google in the United States. Their primary fields of study include computer science and engineering, with a significant focus on artificial intelligence, control and systems engineering, and biomedical engineering. The subfields of their work also encompass computer vision and pattern recognition, as well as aerospace engineering.

The main topics addressed in their research cover reinforcement learning in robotics, robot manipulation and learning, robotic locomotion and control, muscle activation and electromyography studies, human pose and action recognition, biomimetic flight and propulsion mechanisms, and insect behavior and control techniques.

Yuval Tassa has contributed to numerous publications, with notable recent papers including:

  • "dm_control: Software and tasks for continuous control," 2020, Software Impacts
  • "Learning agile soccer skills for a bipedal robot with deep reinforcement learning," 2024, Science Robotics
  • "Catch & Carry," 2020, ACM Transactions on Graphics
  • "From motor control to team play in simulated humanoid football," 2022, Science Robotics
  • "A virtual rodent predicts the structure of neural activity across behaviours," 2024, Nature

The venues in which Yuval Tassa frequently publishes include arXiv (Cornell University), Nature, Science Robotics, Zenodo (CERN European Organization for Nuclear Research), and Software Impacts.

Collaborations form an important aspect of their research activities. Frequent co-authors include Josh Merel, Leonard Hasenclever, Nicolas Heess, Saran Tunyasuvunakool, and Tuomas Haarnoja.

Best Publications

  • Continuous control with deep reinforcement learning

    Timothy P. Lillicrap;Jonathan J. Hunt;Alexander Pritzel;Nicolas Heess

  • MuJoCo: A physics engine for model-based control

    Emanuel Todorov;Tom Erez;Yuval Tassa

  • Emergence of Locomotion Behaviours in Rich Environments

    Nicolas Heess;Dhruva Tb;Srinivasan Sriram;Jay Lemmon

  • Synthesis and stabilization of complex behaviors through online trajectory optimization

    Yuval Tassa;Tom Erez;Emanuel Todorov

  • DeepMind Control Suite

    Yuval Tassa;Yotam Doron;Alistair Muldal;Tom Erez

  • Control-limited differential dynamic programming

    Yuval Tassa;Nicolas Mansard;Emo Todorov

  • Learning continuous control policies by stochastic value gradients

    Nicolas Heess;Greg Wayne;David Silver;Timothy Lillicrap

  • Attend, infer, repeat: fast scene understanding with generative models

    S. M. Ali Eslami;Nicolas Heess;Theophane Weber;Yuval Tassa

  • Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX

    Tom Erez;Yuval Tassa;Emanuel Todorov

  • Safe Exploration in Continuous Action Spaces

    Gal Dalal;Krishnamurthy Dvijotham;Matej Vecerik;Todd Hester

  • Maximum a Posteriori Policy Optimisation

    Abbas Abdolmaleki;Jost Tobias Springenberg;Yuval Tassa;Rémi Munos

  • Whole-body model-predictive control applied to the HRP-2 humanoid

    J. Koenemann;A. Del Prete;Y. Tassa;E. Todorov

  • Learning human behaviors from motion capture by adversarial imitation

    Josh Merel;Yuval Tassa;Dhruva Tb;Sriram Srinivasan

  • Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

    Ivaylo Popov;Nicolas Heess;Timothy P. Lillicrap;Roland Hafner

  • Learning and Transfer of Modulated Locomotor Controllers

    Nicolas Heess;Gregory Wayne;Yuval Tassa;Timothy P. Lillicrap

  • An integrated system for real-time model predictive control of humanoid robots

    Tom Erez;Kendall Lowrey;Yuval Tassa;Vikash Kumar

  • dm_control: Software and Tasks for Continuous Control

    Yuval Tassa;Saran Tunyasuvunakool;Alistair Muldal;Yotam Doron

  • Receding Horizon Differential Dynamic Programming

    Yuval Tassa;Tom Erez;William D. Smart

  • Stochastic Differential Dynamic Programming

    Evangelos Theodorou;Yuval Tassa;Emo Todorov

  • Catch & Carry: reusable neural controllers for vision-guided whole-body tasks

    Josh Merel;Saran Tunyasuvunakool;Arun Ahuja;Yuval Tassa

  • Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts

    Tom Erez;Yuval Tassa;Emanuel Todorov

Frequent Co-Authors

Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Emanuel Todorov
Emanuel Todorov University of Washington
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
David Silver
David Silver DeepMind (United Kingdom)
Martin Riedmiller
Martin Riedmiller DeepMind (United Kingdom)
Jost Tobias Springenberg
Jost Tobias Springenberg University of Freiburg
Vikash Kumar
Vikash Kumar University of Washington
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Nicolas Mansard
Nicolas Mansard Laboratory for Analysis and Architecture of Systems
Javier R. Movellan
Javier R. Movellan University of California, San Diego

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