Divya D. Kulkarni

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About

Divya D. Kulkarni is a researcher working at the intersection of distributed robotics, machine learning, and evolutionary computation, with a focus on enabling intelligent, autonomous multi-robot systems. Her most prominent contribution, "On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios" (2022), advances the field of Federated Learning by eliminating reliance on centralized servers, addressing critical challenges of privacy and bandwidth in collaborative multi-robot environments — a work that has already garnered 13 citations since its publication. Her earlier research explored biologically inspired approaches to robot learning; her 2018 paper draws on immunological principles to develop a distributed, embodied algorithm for action evolution and selection, offering an innovative alternative to traditional Evolutionary Robotics methods that struggle with complex, multi-faceted tasks. Kulkarni has also investigated neuroevolutionary techniques, proposing mutational puissance-assisted methods that refine how neural network weights are updated during evolution. Across her body of work, she demonstrates a consistent drive to decentralize and distribute intelligence in robotic systems, pushing boundaries in both practical deployment and theoretical foundations of autonomous, adaptive machines.