Aakriti Agrawal
Papers
2
Total Citations
29
H-Index
2
About
Aakriti Agrawal is a leading researcher in multi-robot systems, specializing in the intersection of reinforcement learning, task allocation, and decentralized navigation. Her work directly addresses critical challenges in complex, real-world environments, particularly automated warehouses. Agrawal’s major contributions include the development of novel algorithms that enable fleets of robots to coordinate efficiently without a central controller. Her paper "RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments" (2023, 17 citations) introduces a groundbreaking deep multi-agent reinforcement learning method that uses an attention-inspired policy architecture to solve the task allocation problem, formulated as a Markov Decision Process. Building on this, her work "DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments" (2022, 12 citations) presents a fully decentralized algorithm that simultaneously handles task assignment and collision-free navigation for pick-up and delivery tasks. By enabling scalable, robust, and adaptive coordination, Agrawal’s research is paving the way for more intelligent and autonomous robotic fleets, making her a notable figure in advancing practical multi-agent systems.
Research Focus
Key Achievements
Top Papers
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