Papers
2
Total Citations
30
H-Index
2
About
Philipp Benz is a roboticist whose work bridges perception, planning, and real-world autonomy, with a focus on service robots and mobile manipulation. His research centers on deep reinforcement learning for sensor-based navigation and integrated robot control, aiming to make robots practical assistants in human environments. His most cited work, "Sensor-Based Mobile Robot Navigation via Deep Reinforcement Learning" (2018), has garnered 28 citations and explores how deep learning can enable robots to navigate complex, dynamic spaces without pre-mapped routes—a key step toward truly autonomous service robots. In "Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo" (2019), Benz contributed to a system that allows a humanoid robot to perceive its surroundings, plan tasks, and execute actions quickly enough to fetch objects in real time. This work addresses the pressing need for reliable, fast robotic assistance in aging societies. Benz’s contributions are notable for their focus on end-to-end systems that combine learning-based perception with practical execution, demonstrating how robots can move from research labs into everyday service roles.
Research Focus
Key Achievements
Top Papers
- 1Sensor-Based Mobile Robot Navigation via Deep Reinforcement Learning28 citations · 2018
- 2