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About

Suman Chakravorty is a prominent robotics and autonomous systems researcher whose work sits at the intersection of motion planning, probabilistic reasoning, and control under uncertainty. Best known for developing generalized frameworks for sampling-based motion planning, his influential 2011 paper on Generalized Sampling-Based Motion Planners (42 citations) extended foundational algorithms like Probabilistic Roadmaps and Rapidly-exploring Random Trees into hybrid hierarchical architectures capable of handling complex, real-world environments. A central theme throughout his career is enabling robots to act intelligently under uncertainty — a challenge he addresses through belief-space planning, where robots reason over probability distributions rather than exact states. His landmark work on Simultaneous Localization and Planning (SLAP), published in 2018 and accumulating 40 citations, tackled the formidable problem of continuous POMDPs for real-time autonomous navigation. His Feedback-based Information RoadMap (FIRM) contributions further demonstrated robust, online replanning on physical mobile robots (31 citations). More recently, Chakravorty has expanded into soft robotics, applying model- and data-driven control strategies to tensegrity systems (28 citations). Spanning nearly two decades, his research has provided both theoretical foundations and practical algorithms that continue to shape autonomous robotics.