Eugene Auh
Papers
11
Total Citations
92
H-Index
6
About
Eugene Auh is a robotics researcher whose work centers on autonomous navigation, task planning, and manipulation for mobile and dual-arm robotic systems. His major contributions lie in developing intelligent algorithms that enable robots to operate safely and efficiently in complex, real-world environments. Auh has pioneered the use of deep reinforcement learning for collision avoidance in both static and dynamic settings, demonstrating its effectiveness across diverse scenarios. He has also advanced practical logistics applications, such as creating an A-star-based unloading sequence planner for autonomous container systems and a vision system for box handling. His research on online task scheduling using mixed-integer programming for dual-arm cooking robots showcases his ability to tackle multi-tasking challenges. With over 90 citations to his most-cited papers, Auh’s impact is evident in his highly cited work on autonomous unloading (22 citations) and cooking task planning (16 citations). His recent integration of radar-based obstacle detection with deep reinforcement learning, along with his comparative analysis of reward functions, further solidifies his reputation as a leading innovator in robust, real-time robot navigation.
Research Focus
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Top Papers
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