Max Robotics — Global Service Robot Directory + US Certification

About

Sayantan Auddy is a researcher working at the intersection of robotics, reinforcement learning, and human-robot interaction. His work spans two compelling directions: developing intelligent assistive robotic systems and advancing simulation-to-real-world transfer techniques for autonomous agents. His earliest notable contribution, "A Robotic Home Assistant with Memory Aid Functionality" (2016), demonstrated practical applications of robotics in everyday assistive contexts, accumulating 16 citations and establishing his foundation in socially relevant human-robot interaction. More recently, Auddy has turned his attention to a fundamental challenge in modern robotics — the sim2real gap — through his work on "Continual Domain Randomization" (2024), which innovates upon traditional domain randomization by addressing the constraints of fixed simulator parameters during training. This approach enables more flexible and robust policy learning for real-world deployment of reinforcement learning agents. With a research trajectory that bridges assistive technology and cutting-edge machine learning methodology, Auddy represents a researcher committed to making autonomous systems both practically useful and technically sophisticated. His growing body of work positions him as an emerging contributor to the robotics and sim2real transfer communities.