Luke Burks
Papers
3
Total Citations
42
H-Index
3
About
Luke Burks is a researcher specializing in human-robot teaming, autonomous planning, and semantic data fusion — fields at the intersection of artificial intelligence, robotics, and human-autonomy collaboration. His work addresses one of the most pressing challenges in modern robotics: enabling autonomous systems to effectively incorporate and act upon ambiguous, human-provided "soft data" alongside traditional sensor measurements. Burks's most notable contribution is the development of the Human-Assisted Robotic Planning and Sensing (HARPS) framework, which leverages Partially Observable Markov Decision Process (POMDP) planning to formally unify human semantic input with robotic decision-making. This work, which has garnered 11 citations since its 2023 publication, represents a significant step toward practical human-robot collaboration in real-world, partially known environments. His earlier foundational research on collaborative semantic sensing and soft-hard data fusion, published as early as 2019, laid the groundwork for these advances and has accumulated 10 citations. His most-cited paper, with 21 citations, further demonstrates the value of structured POMDP approaches for joint human-autonomy sensing tasks. Collectively, Burks's research provides a rigorous, mathematically grounded pathway for deploying intelligent robotic systems alongside humans in complex, dynamic scenarios such as search and rescue operations.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3