Ankush Chakrabarty
Mitsubishi Electric (Japan), Mitsubishi Electric (United States)
Papers
2
Total Citations
12
H-Index
2
About
Ankush Chakrabarty is a leading researcher at the intersection of robotics, control theory, and machine learning, with a primary focus on developing scalable algorithms for multi-robot coordination and state estimation. His most influential work, "Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs" (2021, 9 citations), introduces a groundbreaking approach that combines imitation learning with mixed-integer programming (MIP). By training a neural network to replicate optimal MIP solutions, Chakrabarty enables real-time trajectory planning for multiple robots—a critical advance for applications in warehouse automation and autonomous fleets. This work demonstrates his signature ability to bridge computational optimization with practical, real-time execution. In parallel, his 2024 paper on "LMI-based neural observer for state and nonlinear function estimation" (3 citations) tackles a fundamental challenge in control theory: estimating unknown system dynamics. Here, he develops a neuro-adaptive observer that guarantees exponentially stable error convergence, offering a robust solution for systems with partially modeled dynamics. Chakrabarty’s contributions are notable for their dual emphasis on theoretical rigor and deployable efficiency, making him a rising figure in the robotics and controls community. His work continues to shape how intelligent systems learn, plan, and adapt in complex, uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2