Kaustubh Joshi
Papers
2
Total Citations
10
H-Index
2
About
Kaustubh Joshi is a robotics researcher whose work lies at the intersection of autonomous navigation, bio-inspired perception, and human-robot collaboration. His primary research areas include vision-based underwater navigation, multi-robot coordination, and gesture-based interaction systems. Joshi’s most notable contribution is **UIVNAV** (Underwater Information-driven Vision-based Navigation via Imitation Learning), a framework that tackles the formidable challenges of autonomous underwater movement—limited visibility, dynamic environments, and the absence of reliable localization. By leveraging imitation learning, UIVNAV enables cost-efficient, vision-driven navigation without dependence on expensive acoustic sensors, a breakthrough for marine exploration and monitoring. This work has already garnered 8 citations since its 2024 publication. In a complementary vein, Joshi has advanced human-cooperative robotics with a bio-inspired vision and gesture-based system for robot-robot interaction in package delivery. This framework eliminates the need for network-based communication, allowing robots to coordinate through visual cues and gestures in constrained environments. His research demonstrates a clear trajectory toward making autonomous systems more resilient, intuitive, and adaptable—whether beneath the waves or in human-shared spaces.
Research Focus
Key Achievements
Top Papers
- 1
- 2