Ravi N. Haksar
Papers
8
Total Citations
173
H-Index
5
About
Ravi N. Haksar is a leading researcher in multi-robot systems and autonomous decision-making under constraints, with a focus on distributed control and reinforcement learning for large-scale spatial processes. His most impactful work, "Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots" (78 citations), pioneers a scalable RL framework for coordinating UAV teams to combat wildfires, modeling the problem as a factored Markov decision process. Haksar also made significant contributions to multi-robot task allocation, developing consensus-based ADMM algorithms that enable efficient, distributed assignment of robots to tasks (47 citations). His research extends to controlling graph-based MDPs with global resource constraints, addressing challenges like limited firefighting resources or measurement uncertainty in dynamic environments. Notably, Haksar has explored bio-inspired robotics, studying how cats orient in mid-air to achieve safe landings—a concept with implications for fall recovery in agile robots. With over 170 total citations, his work bridges theory and practice, offering scalable solutions for environmental monitoring, disaster response, and swarm robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Distributed Multirobot Task Assignment via Consensus ADMM47 citations · 2023
- 3
- 4Consensus-Based ADMM for Task Assignment in Multi-robot Teams12 citations · 2022
- 5
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- 7Scalable Filtering of Large Graph-Coupled Hidden Markov Models3 citations · 2019
- 8