Jeremy Muesing
Papers
1
Total Citations
21
H-Index
1
About
Jeremy Muesing is a researcher at the forefront of human-autonomy interaction, with a focus on developing intelligent systems that can seamlessly collaborate with humans in complex, uncertain environments. His work centers on structured planning under partial observability, particularly through the lens of Partially Observable Markov Decision Processes (POMDPs). His most-cited paper, "Collaborative human-autonomy semantic sensing through structured POMDP planning" (2021, 21 citations), introduces a novel framework that enables autonomous agents to actively query humans for semantic information—such as object labels or spatial relationships—while reasoning about the human's knowledge and intent. This contribution is pivotal for applications like assistive robotics and autonomous driving, where shared understanding between human and machine is critical. Muesing's approach leverages structured representations to make POMDP planning computationally tractable, addressing a key bottleneck in real-time human-robot collaboration. His work has been recognized for bridging theoretical planning algorithms with practical sensing tasks, earning citations from researchers in robotics, AI, and human factors. By advancing methods for collaborative semantic sensing, Muesing is helping to shape a future where autonomous systems can intuitively and efficiently work alongside people.
Research Focus
Key Achievements
Top Papers
- 1