Abhish Khanal
Papers
1
Total Citations
3
H-Index
1
About
Abhish Khanal is a rising researcher in robotics and artificial intelligence, whose work centers on multi-robot systems, long-horizon navigation, and planning under uncertainty. His key contributions lie in developing learning-augmented algorithms that enable teams of robots to efficiently navigate through partially mapped, unknown environments. In his most cited work, "Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments" (2023), Khanal introduces a novel approach that predicts statistics of unknown space, allowing robots to make informed, goal-directed decisions over extended time horizons. This work bridges model-based planning with machine learning, significantly improving reliability and efficiency in real-world deployment. With 3 citations to date, this paper is gaining traction in the multi-robot systems community. Khanal’s research has implications for search-and-rescue, exploration, and autonomous logistics, where teams of robots must coordinate in unstructured settings. His work stands out for its practical focus on reducing uncertainty and enhancing team coordination, marking him as a promising young scholar pushing the boundaries of autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1