Ansuma Basumatary

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About

Ansuma Basumatary’s research lies at the intersection of combinatorial optimization, robotics, and artificial intelligence, with a core focus on solving real-world logistics challenges through reinforcement learning. Her most influential work, “A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing” (2020, 7 citations), introduces a deep RL framework capable of handling arbitrary numbers of bins and sizes. Crucially, this algorithm is designed for physical implementation, generating packing decisions executable by a robotic loading arm—a significant step toward automating warehouse operations. Building on this foundation, her 2022 study (3 citations) addresses the computational bottlenecks of real-time packing by leveraging FPGA acceleration, demonstrating how hardware-software co-design can boost the performance of RL algorithms in time-critical environments. This work tackles the practical challenge of packing rigid cuboids arriving on a conveyor into a bin, a problem central to modern e-commerce and shipping. By bridging algorithmic innovation with hardware efficiency, Basumatary’s contributions offer scalable, deployable solutions for automated logistics, making her research particularly valuable for students and engineers working on intelligent robotic systems and real-time optimization.