Philipp S. Schmitt
Papers
8
Total Citations
144
H-Index
5
About
Philipp S. Schmitt is a leading researcher in robotic manipulation, focusing on the intersection of motion planning, state estimation, and control for complex, contact-rich tasks. His major contributions lie in developing algorithms that enable robots to reason about and execute manipulation in dynamic, partially observable environments—bridging the gap between high-level task planning and low-level reactive control. His seminal work on "Optimal, sampling-based manipulation planning" (50 citations) introduced a framework for simultaneously reasoning about robot and object motion in high-dimensional spaces, a foundational approach in the field. He further advanced the state of the art with his kinodynamic manipulation planner for dynamic environments (26 citations) and a Bayesian state estimator for contact-rich tasks (20 citations), which explicitly models contact dynamics and torque-based control. Schmitt’s integrated system for controlling manipulation under partial observability (14 citations) demonstrates a practical, model-based approach to tracking belief states and generating robust motions. His work on constraint-based task specification and time-optimal trajectory optimization (2022) continues to push toward efficient, deployable robotic systems, making him a key figure in modern manipulation research.
Research Focus
Key Achievements
Top Papers
- 1Optimal, sampling-based manipulation planning50 citations · 2017
- 2Modeling and Planning Manipulation in Dynamic Environments26 citations · 2019
- 3Planning Reactive Manipulation in Dynamic Environments21 citations · 2019
- 4State Estimation in Contact-Rich Manipulation20 citations · 2019
- 5Controlling Contact-Rich Manipulation Under Partial Observability14 citations · 2020
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- 8Robust, Compliant Assembly with Elastic Parts and Model Uncertainty4 citations · 2019