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Computer Science
Sweden
2026
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Electronics and Electrical Engineering
Sweden
2026

D-Index & Metrics

Computer Science

D-Index
74
Citations
22608
World Ranking
1491
National Ranking
3

Electronics and Electrical Engineering

D-Index
74
Citations
22287
World Ranking
710
National Ranking
10

Research.com Recognitions

  • 2026 - Research.com Computer Science in Sweden Leader Award
  • 2026 - Research.com Electronics and Electrical Engineering in Sweden Leader Award
  • 2025 - Research.com Computer Science in Sweden Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in Sweden Leader Award
  • 2023 - Research.com Computer Science in Sweden Leader Award
  • 2022 - Research.com Computer Science in Sweden Leader Award
  • 2016 - IEEE Fellow For contributions to vision-based systems and robotic object manipulation

Overview

Danica Kragic is affiliated with the Royal Institute of Technology in Sweden. Their primary research spans across computer science and engineering, with significant contributions to control and systems engineering, artificial intelligence, and computer vision and pattern recognition. The scientist's work also extends into biomedical engineering and cognitive neuroscience.

The main topics of research for Danica Kragic include robot manipulation and learning, with 74 publications; reinforcement learning in robotics, with 40 publications; human pose and action recognition; human motion and animation; domain adaptation and few-shot learning; 3D shape modeling and analysis; and robotic path planning algorithms.

Frequent publication venues in which Danica Kragic has contributed include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • Science Robotics
  • IEEE Transactions on Robotics
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Coauthors frequently collaborating with Danica Kragic consist of Michael C. Welle, Mårten Björkman, Anastasiia Varava, Hang Yin, and Petra Poklukar.

Recent papers authored or coauthored by Danica Kragic include:

  • Combating COVID-19-The role of robotics in managing public health and infectious diseases, 2020, Science Robotics
  • Modeling, learning, perception, and control methods for deformable object manipulation, 2021, Science Robotics
  • Deep Learning Approaches to Grasp Synthesis: A Review, 2023, IEEE Transactions on Robotics
  • Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review, 2020, Frontiers in Robotics and AI
  • Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement, 2024, Nature Communications

Danica Kragic was awarded the IEEE Fellow distinction in 2016 for contributions to vision-based systems and robotic object manipulation.

Best Publications

  • Data-Driven Grasp Synthesis—A Survey

    Jeannette Bohg;Antonio Morales;Tamim Asfour;Danica Kragic

  • The GRASP Taxonomy of Human Grasp Types

    Thomas Feix;Javier Romero;Heinz-Bodo Schmiedmayer;Aaron M. Dollar

  • Trends and challenges in robot manipulation

    Aude Billard;Danica Kragic

  • IEEE International Conference on Robotics and Automation (ICRA)

    Johannes A Stork;Carl Henrik Ek;Yasemin Bekiroglu;Danica Kragic

  • IEEE/RSJ International Conference on Intelligent Robots and Systems

    Marianna Madry;Heydar Maboudi Afkham;Carl Henrik Ek;Stefan Carlsson

  • Short survey: Dual arm manipulation-A survey

    Christian Smith;Yiannis Karayiannidis;Lazaros Nalpantidis;Xavi Gratal

  • Combating COVID-19-The role of robotics in managing public health and infectious diseases.

    Guang-Zhong Yang;Bradley J. Nelson;Robin R. Murphy;Howie Choset

  • Deep Representation Learning for Human Motion Prediction and Classification

    Judith Butepage;Michael J. Black;Danica Kragic;Hedvig Kjellstrom

  • Interactive Perception: Leveraging Action in Perception and Perception in Action

    Jeannette Bohg;Karol Hausman;Bharath Sankaran;Oliver Brock

  • Survey on Visual Servoing for Manipulation

    Danica Kragic;Henrik I Christensen

  • Visual object-action recognition: Inferring object affordances from human demonstration

    Hedvig Kjellström;Javier Romero;Danica Kragić

  • Modeling, learning, perception, and control methods for deformable object manipulation

    Hang Yin;Anastasia Varava;Danica Kragic

  • Deep Learning Approaches to Grasp Synthesis: A Review

    Unknown

  • Design of a flexible tactile sensor for classification of rigid and deformable objects

    Alin Drimus;Gert Kootstra;Arne Bilberg;Danica Kragic

  • Minimum volume bounding box decomposition for shape approximation in robot grasping

    K. Huebner;S. Ruthotto;D. Kragic

  • Assessing Grasp Stability Based on Learning and Haptic Data

    Yasemin Bekiroglu;Janne Laaksonen;Jimmy Alison Jorgensen;Ville Kyrki

  • The meaning of action: a review on action recognition and mapping

    Volker Krüger;Danica Kragic;Aleš Ude;Christopher Geib

  • Retracted: Human-Machine Collaborative Systems for Microsurgical Applications

    Danica Kragic;Panadda Marayong;Ming Li;Allison M. Okamura

  • Grasp Recognition for Programming by Demonstration

    S. Ekvall;D. Kragic

  • Learning grasping points with shape context

    Jeannette Bohg;Danica Kragic

  • Interactive grasp learning based on human demonstration

    S. Ekvall;D. Kragic

  • IEEE International Conference on Robotics and Automation

    Mitesh Patel;Carl Henrik Ek;Nikolaos Kyriazis;Antonis Argyros

  • IEEE-RAS International Conference on Humanoid Robots

    Niklas Bergström;Carl Henrik Ek;Danica Kragic;Yuji Yamakawa

Frequent Co-Authors

Henrik I. Christensen
Henrik I. Christensen University of California, San Diego
Jeannette Bohg
Jeannette Bohg Stanford University
Patric Jensfelt
Patric Jensfelt Royal Institute of Technology
Javier Romero
Javier Romero Facebook (United States)
Tamim Asfour
Tamim Asfour Karlsruhe Institute of Technology
Norbert Krüger
Norbert Krüger University of Southern Denmark
Gregory D. Hager
Gregory D. Hager Johns Hopkins University
Ken Goldberg
Ken Goldberg University of California, Berkeley
Aude Billard
Aude Billard École Polytechnique Fédérale de Lausanne
Xiaoming Hu
Xiaoming Hu Royal Institute of Technology

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