Aman Khakharia
Papers
1
Total Citations
2
H-Index
1
About
Aman Khakharia is a researcher whose work lies at the intersection of robotics, probabilistic modeling, and biomedical engineering. His most notable contribution is the development of the Probabilistic Collision Hidden Markov Model (PCHMM), a novel framework for robotic motion planning that enables contactless drug delivery. This approach integrates hidden Markov models with collision probability estimation, allowing robots to navigate dynamic environments with high precision and safety—critical for delicate medical procedures. While his 2021 paper has garnered 2 citations, its conceptual novelty has laid groundwork for safer human-robot interaction in healthcare settings. Khakharia’s research addresses the growing need for non-invasive automation in medicine, where minimizing physical contact reduces infection risks and patient trauma. His work exemplifies how probabilistic methods can bridge the gap between theoretical robotics and real-world clinical applications. As a researcher, Khakharia continues to explore the frontiers of autonomous systems, with a focus on enhancing reliability and adaptability in high-stakes environments.
Research Focus
Key Achievements
Top Papers
- 1