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About

Shoubhik Debnath is a robotics and artificial intelligence researcher whose work spans transparent object perception, bio-inspired motor control, and reinforcement learning. His most impactful contribution is the RGB-D Local Implicit Function for depth completion of transparent objects—a breakthrough addressing a critical limitation of standard 3D sensors, which fail to capture depth data from glass, plastic, and other transparent materials due to refraction and light absorption. This work, garnering 85 citations, enables robots to perceive and manipulate transparent objects reliably, with direct applications in manufacturing, service robotics, and autonomous navigation. Debnath also developed a Multi-Layered Multi-Pattern Central Pattern Generator (CPG) for humanoid robots, allowing a single controller to generate diverse motor patterns for tasks like reaching and writing, advancing the field of bio-inspired locomotion. His research extends to solving Markov decision processes using Mean First Passage Time to characterize state reachability, and to modeling cortical-basal reinforcement learning with success-failure experience, bridging computational neuroscience and robotics. With a portfolio that combines practical perception solutions with foundational algorithmic and neurocognitive models, Debnath’s work demonstrates a rare ability to tackle both immediate engineering challenges and long-term questions in intelligent, adaptive systems.