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About

Arun Baran Samaddar is a researcher whose work sits at the intersection of autonomous robotics, neural computation, and human-robot interaction. His primary contributions focus on mobile robot navigation and complete coverage path planning in grid-based environments, challenges that are central to real-world applications such as warehouse automation, smart city traffic planning, and military operations. Samaddar has developed neural dynamics-based approaches that enable both single and multiple mobile robots to navigate collision-free paths while achieving comprehensive environmental coverage — a technically demanding problem requiring sophisticated handling of dynamic obstacles and inter-robot coordination. His 2017 paper on local and target weighted neural networks for mobile robot navigation represents a foundational contribution to the field, while subsequent work published through 2020 and 2021 extended these methods to multi-robot systems. Beyond robotics navigation, Samaddar has explored the frontier of natural language processing applied to physical embodiment, contributing a text-to-animation system that translates exercise instructions into actionable robot behaviors — an important step toward automating skill acquisition. With a growing citation record across multiple venues, his research addresses practical, high-impact problems that bridge theoretical neural computation and applied autonomous systems.