Niels van Duijkeren
Papers
11
Total Citations
140
H-Index
7
About
Niels van Duijkeren is a robotics researcher whose work spans motion planning, model predictive control, and machine learning for robotic manipulation and mobile robotics. His early research established significant contributions to time-optimal motion planning for robotic manipulators, developing path-parametric system reformulations that enable robots to move faster by exploiting small deviations from predefined Cartesian paths — work that has accumulated over 30 citations each. Complementing this, his nonlinear model predictive control (NMPC) framework for path-following strikes a principled balance between tracking accuracy and execution speed. Beyond classical control theory, van Duijkeren has pushed into data-driven robotics, developing hybrid inverse dynamics models that blend rigid body physics with learned components to achieve precise impedance control, and action-conditional recurrent networks for dynamics learning. His work on Learning from Demonstration extended task-parameterized methods to forceful, multi-modal manipulation skills — an important step toward practical robot programming. On the mobile robotics side, he has contributed robust MPC approaches for collision-free navigation and caster-wheel-aware motion planning. With papers spanning industrial assembly, multi-agent coordination, and end-to-end learning, van Duijkeren represents a researcher bridging rigorous control theory with modern machine learning to advance real-world robotic autonomy.
Research Focus
Key Achievements
Top Papers
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- 3Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations22 citations · 2021
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- 9A caster-wheel-aware MPC-based motion planner for mobile robotics6 citations · 2021
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