Lucas Janson
Papers
10
Total Citations
373
H-Index
5
About
Lucas Janson is a robotics researcher whose work has significantly advanced the theoretical and practical foundations of motion planning and robot manipulation. His most influential contributions lie in sampling-based motion planning algorithms, where he has worked to bridge the gap between probabilistic methods and deterministic guarantees. His landmark work on deterministic sampling-based motion planning — which has accumulated over 160 citations across multiple versions — rigorously analyzes the optimality, complexity, and performance of algorithms like PRM and RRT, establishing stronger theoretical footing for tools widely used in robotic navigation. Complementing this, his development of Monte Carlo Motion Planning (MCMP), cited over 100 times, introduced a principled framework for trajectory optimization under uncertainty, enabling robots to navigate while satisfying probabilistic collision-avoidance constraints. His research on differential constraints further extended optimal planning guarantees to dynamically complex systems. More recently, Janson has expanded into robot manipulation and tactile sensing, investigating how reinforcement learning and analytic grasp stability metrics can drive intelligent grasp refinement. Across these domains, his work consistently combines mathematical rigor with practical relevance, making him a notable contributor to modern autonomous robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty105 citations · 2017
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- 8Map-Predictive Motion Planning in Unknown Environments2 citations · 2020
- 9
- 10Tactile Grasp Refinement using Deep Reinforcement Learning and Analytic Grasp Stability Metrics.2 citations · 2021