Oscar de Groot

About

Oscar de Groot is a robotics researcher specializing in motion planning and trajectory optimization for autonomous robots operating in dynamic, human-inhabited environments. His work addresses one of the most pressing challenges in autonomous navigation: enabling robots to move safely and efficiently when surrounded by unpredictable humans and obstacles. De Groot's most significant contributions center on probabilistic and optimization-based planning frameworks. His highly cited 2021 paper on scenario-based trajectory optimization (29 citations) introduced a method for bounding collision risk through chance constraints, providing rigorous safety guarantees under uncertainty. Building on this foundation, he developed topology-driven parallel optimization techniques (20 citations) that address the persistent problem of local optima in nonconvex trajectory planning, enabling robots to consider multiple passing behaviors simultaneously. His work on globally guided planning (15 citations) further tackles the challenge of selecting optimal trajectories in nonconvex, human-shared spaces. Beyond geometric safety, de Groot has explored socially aware navigation through visually guided motion planning learned from demonstrations (15 citations), reflecting a commitment to context-sensitive robot behavior. His probabilistic risk assessment and scenario-based planning research collectively form a coherent research program with over 100 cumulative citations, establishing him as an emerging authority in safe autonomous navigation.

Research Focus

Key Achievements

Top Papers

1. 1
   Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments

   29 citations · 2021
2. 2
   Topology-Driven Parallel Trajectory Optimization in Dynamic Environments

   20 citations · 2024
3. 3
   Visually-guided motion planning for autonomous driving from interactive demonstrations

   15 citations · 2022
4. 4
   Globally Guided Trajectory Planning in Dynamic Environments

   15 citations · 2023
5. 5
   Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments

   11 citations · 2023
6. 6
   Scenario-based motion planning with bounded probability of collision

   10 citations · 2025
7. 7
   Scenario-Based Motion Planning with Bounded Probability of Collision

   6 citations · 2023
8. 8
   Topology-Driven Parallel Trajectory Optimization in Dynamic Environments

   3 citations · 2024
9. 9
   Probabilistic Motion Planning and Prediction via Partitioned Scenario Replay

   2 citations · 2024

Key Collaborators

Contact & Links