Patrick Lehmann
Papers
2
Total Citations
11
H-Index
2
About
Patrick Lehmann is a researcher in autonomous robotics, focusing on the intersection of decision-making, planning, and adaptive control systems. His work centers on developing hybrid architectures that enable robots to operate effectively in dynamic, multi-agent environments. Lehmann’s major contributions include advancing task-level decision-making and AI planning frameworks, particularly through the integration of reinforcement learning to enhance self-adaptation. His 2019 paper on increasing self-adaptation in hybrid systems demonstrates how dynamic action selection allows robots to respond to environmental and internal state changes, improving mission-oriented autonomy. His 2018 work on applying robotic frameworks in simulated multi-agent contests, with 6 citations, provides foundational insights into competitive robotic coordination. Though his citation counts are modest, Lehmann’s research is notable for bridging theoretical planning with practical reinforcement learning, offering scalable solutions for autonomous systems. His achievements include pioneering methods for real-time adaptation in robotic decision-making, contributing to the broader field of intelligent, self-adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1Applying robotic frameworks in a simulated multi-agent contest6 citations · 2018
- 2