World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
31092
World Ranking
5995
National Ranking
363

Overview

Raia Hadsell is affiliated with DeepMind in the United Kingdom and has a research portfolio focused primarily on artificial intelligence and robotics. Their academic contributions span several subfields within computer science and engineering, notably artificial intelligence, computer vision and pattern recognition, biomedical engineering, control and systems engineering, and atmospheric science.

Hadsell's main fields of study include:

  • Computer Science
  • Engineering

The scientist's work addresses a variety of research topics, including:

  • Reinforcement Learning in Robotics
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Robot Manipulation and Learning
  • Robotic Locomotion and Control
  • Multimodal Machine Learning Applications
  • Advanced Sensor and Energy Harvesting Materials

Raia Hadsell has published extensively, with notable frequent publication venues such as:

  • arXiv (Cornell University)
  • Nature
  • Trends in Cognitive Sciences
  • Science Robotics
  • IEEE Robotics and Automation Letters

Highlighted recent papers include:

  • Skilful precipitation nowcasting using deep generative models of radar, 2021, Nature
  • Embracing Change: Continual Learning in Deep Neural Networks, 2020, Trends in Cognitive Sciences
  • Distral: Robust Multitask Reinforcement Learning, 2025, Oxford University Research Archive (University of Oxford)
  • Learning agile soccer skills for a bipedal robot with deep reinforcement learning, 2024, Science Robotics
  • A Generalist Agent, 2022, arXiv (Cornell University)

Frequent co-authors in their research include:

  • Nicolas Heess
  • Dushyant Rao
  • Razvan Pascanu
  • Markus Wulfmeier
  • Leonard Hasenclever

Best Publications

  • Overcoming catastrophic forgetting in neural networks

    James Kirkpatrick;Razvan Pascanu;Neil C. Rabinowitz;Joel Veness

  • Dimensionality Reduction by Learning an Invariant Mapping

    R. Hadsell;S. Chopra;Y. LeCun

  • Learning a similarity metric discriminatively, with application to face verification

    S. Chopra;R. Hadsell;Y. LeCun

  • Progressive neural networks

    Neil Charles Rabinowitz;Guillaume Desjardins;Andrei-Alexandru Rusu;Koray Kavukcuoglu

  • A Tutorial on Energy-Based Learning

    Yann LeCun;Sumit Chopra;Raia Hadsell;Aurelio Ranzato

  • Meta-Learning with Latent Embedding Optimization

    Andrei A. Rusu;Dushyant Rao;Jakub Sygnowski;Oriol Vinyals

  • Skilful precipitation nowcasting using deep generative models of radar.

    Suman V. Ravuri;Karel Lenc;Matthew Willson;Dmitry Kangin;Dmitry Kangin

  • Vector-based navigation using grid-like representations in artificial agents

    Andrea Banino;Caswell Barry;Benigno Uria;Charles Blundell

  • Learning to Navigate in Complex Environments

    Piotr Mirowski;Razvan Pascanu;Fabio Viola;Hubert Soyer

  • The limits and potentials of deep learning for robotics

    Niko Sünderhauf;Oliver Brock;Walter J. Scheirer;Raia Hadsell

  • A Generalist Agent

    Unknown

  • Sim-to-Real Robot Learning from Pixels with Progressive Nets

    Andrei A. Rusu;Matej Vecerík;Thomas Rothörl;Nicolas Heess

  • Learning long-range vision for autonomous off-road driving

    Raia Hadsell;Pierre Sermanet;Jan Ben;Ayse Erkan

  • Embracing Change: Continual Learning in Deep Neural Networks

    Raia Hadsell;Dushyant Rao;Andrei A. Rusu;Razvan Pascanu

  • Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks

    Stephen James;Paul Wohlhart;Mrinal Kalakrishnan;Dmitry Kalashnikov

  • Graph Networks as Learnable Physics Engines for Inference and Control

    Alvaro Sanchez-Gonzalez;Nicolas Heess;Jost Tobias Springenberg;Josh Merel

  • Distral: robust multitask reinforcement learning

    Yee Whye Teh;Victor Bapst;Wojciech Marian Czarnecki;John Quan

  • Progress & Compress: A scalable framework for continual learning

    Jonathan Schwarz;Jelena Luketina;Wojciech M. Czarnecki;Agnieszka Grabska-Barwinska

  • Progress & Compress: A scalable framework for continual learning

    Jonathan Schwarz;Wojciech Czarnecki;Jelena Luketina;Agnieszka Grabska-Barwinska

  • Gemma 2: Improving Open Language Models at a Practical Size

    Unknown

  • Reinforcement and Imitation Learning for Diverse Visuomotor Skills

    Yuke Zhu;Ziyu Wang;Josh Merel;Andrei A. Rusu

  • Continual Unsupervised Representation Learning.

    Dushyant Rao;Francesco Visin;Andrei A. Rusu;Razvan Pascanu

Frequent Co-Authors

Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Yann LeCun
Yann LeCun Facebook (United States)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Martin Riedmiller
Martin Riedmiller DeepMind (United Kingdom)
Supun Samarasekera
Supun Samarasekera SRI International
Yee Whye Teh
Yee Whye Teh University of Oxford
Dharshan Kumaran
Dharshan Kumaran Google (United States)
Michael Milford
Michael Milford Queensland University of Technology
Jost Tobias Springenberg
Jost Tobias Springenberg University of Freiburg

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