World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4024
World Ranking
12771
National Ranking
624

Overview

Freek Stulp is affiliated with the German Aerospace Center in Germany and has contributed extensively to the fields of engineering and computer science. Their research spans multiple subfields including control and systems engineering, biomedical engineering, computer vision and pattern recognition, mechanical engineering, and artificial intelligence.

The scientist's work focuses on a range of main topics related to robotics and automation. These include:

  • Robot Manipulation and Learning
  • Teleoperation and Haptic Systems
  • Reinforcement Learning in Robotics
  • Soft Robotics and Applications
  • Tactile and Sensory Interactions
  • Modular Robots and Swarm Intelligence
  • Robotic Locomotion and Control

Freek Stulp has published frequently in notable venues such as:

  • IEEE Robotics and Automation Letters
  • arXiv (Cornell University)
  • Frontiers in Robotics and AI
  • Experimental Brain Research
  • IEEE Robotics & Automation Magazine

Recent papers include:

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models, 2023, arXiv (Cornell University)
  • Object-Level Impedance Control for Dexterous In-Hand Manipulation, 2020, IEEE Robotics and Automation Letters
  • Pattern Recognition for Knowledge Transfer in Robotic Assembly Sequence Planning, 2020, IEEE Robotics and Automation Letters
  • A Digital Twin Approach for Contextual Assistance for Surgeons During Surgical Robotics Training, 2021, Frontiers in Robotics and AI
  • Sensorimotor performance and haptic support in simulated weightlessness, 2020, Experimental Brain Research

Freek Stulp has collaborated frequently with several coauthors, including:

  • Alin Albu-Schäffer
  • João Silvério
  • Gabriel Quere
  • Daniel Leidner
  • Jörn Vogel

The scientist's publication record reflects a diversity of topics and a strong presence in robotics research, particularly in manipulation, haptics, and learning systems. Their contributions intersect both theoretical and applied research relevant to advancing autonomous and assistive robotic technologies.

Best Publications

  • Learning variable impedance control

    Jonas Buchli;Freek Stulp;Evangelos Theodorou;Stefan Schaal

  • Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation

    F. Stulp;E. A. Theodorou;S. Schaal

  • Many regression algorithms, one unified model

    Freek Stulp;Olivier Sigaud

  • Path Integral Policy Improvement with Covariance Matrix Adaptation

    Freek Stulp;Freek Stulp;Olivier Sigaud

  • Robot Skill Learning: From Reinforcement Learning to Evolution Strategies

    Freek Stulp;Olivier Sigaud

  • A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials

    Konstantinos Chatzilygeroudis;Vassilis Vassiliades;Freek Stulp;Sylvain Calinon

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

    Unknown

  • Learning to grasp under uncertainty

    Freek Stulp;Evangelos Theodorou;Jonas Buchli;Stefan Schaal

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

    Unknown

  • Hierarchical reinforcement learning with movement primitives

    Freek Stulp;Stefan Schaal

  • Movement segmentation using a primitive library

    Franziska Meier;Evangelos Theodorou;Freek Stulp;Stefan Schaal

  • The Assistive Kitchen — A demonstration scenario for cognitive technical systems

    M. Beetz;F. Stulp;B. Radig;J. Bandouch

  • From dynamic movement primitives to associative skill memories

    Peter Pastor;Mrinal Kalakrishnan;Franziska Meier;Freek Stulp

  • Variable Impedance Control - A Reinforcement Learning Approach

    Jonas Buchli;Evangelos A. Theodorou;Freek Stulp;Stefan Schaal

  • Learning compact parameterized skills with a single regression

    Freek Stulp;Gennaro Raiola;Antoine Hoarau;Serena Ivaldi

  • Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning

    Freek Stulp;Olivier Sigaud

  • Policy search in continuous action domains: An overview.

    Olivier Sigaud;Freek Stulp

  • Generality and legibility in mobile manipulation

    Michael Beetz;Freek Stulp;Piotr Esden-Tempski;Andreas Fedrizzi

  • Learning Local Objective Functions for Robust Face Model Fitting

    M. Wimmer;F. Stulp;S. Pietzsch;B. Radig

  • Model-Free Reinforcement Learning of Impedance Control in Stochastic Environments

    F. Stulp;J. Buchli;A. Ellmer;M. Mistry

  • Reinforcement learning of full-body humanoid motor skills

    Freek Stulp;Jonas Buchli;Evangelos Theodorou;Stefan Schaal

  • Iteratively Refined Feasibility Checks in Robotic Assembly Sequence Planning

    Ismael Rodriguez;Korbinian Nottensteiner;Daniel Leidner;Michael Kasecker

  • Learning and reasoning with action-related places for robust mobile manipulation

    Freek Stulp;Andreas Fedrizzi;Lorenz Mösenlechner;Michael Beetz

Frequent Co-Authors

Michael Beetz
Michael Beetz University of Bremen
Olivier Sigaud
Olivier Sigaud Sorbonne University
Stefan Schaal
Stefan Schaal Google (United States)
Evangelos A. Theodorou
Evangelos A. Theodorou Georgia Institute of Technology
Pierre-Yves Oudeyer
Pierre-Yves Oudeyer French Institute for Research in Computer Science and Automation - INRIA
Jonas Buchli
Jonas Buchli DeepMind (United Kingdom)
Alin Albu-Schaffer
Alin Albu-Schaffer German Aerospace Center
Jean-Baptiste Mouret
Jean-Baptiste Mouret University of Lorraine
Timothy M. Hospedales
Timothy M. Hospedales University of Edinburgh
Sylvain Calinon
Sylvain Calinon Idiap Research Institute

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