Papers
1
Total Citations
22
H-Index
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About
Manav Mishra is a leading researcher at the intersection of robotics, multi-agent systems, and reinforcement learning. His primary focus lies in developing intelligent coordination strategies for heterogeneous robot teams operating under severe real-world constraints—particularly in environments where GPS is unavailable. His most cited work, “Multi-Agent Deep Reinforcement Learning for Persistent Monitoring With Sensing, Communication, and Localization Constraints” (2024, 22 citations), tackles the formidable challenge of enabling multi-robot persistent monitoring when robots face limited sensing, intermittent communication, and degraded localization. Mishra’s key contribution is a deep reinforcement learning framework that allows a heterogeneous robotic system to jointly learn motion policies that satisfy these intertwined constraints, ensuring robust, long-duration surveillance without external positioning infrastructure. This work is pivotal for applications in disaster response, subterranean exploration, and autonomous surveillance in denied environments. By explicitly modeling the coupling between communication and localization, Mishra has advanced the theoretical and practical foundations of resilient multi-agent autonomy. His research is already shaping how next-generation robot swarms are designed for safety-critical, infrastructure-free operations, marking him as an emerging leader in field-deployable multi-robot intelligence.
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Top Papers
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