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About

Boris Belousov is a robotics researcher whose work sits at a compelling intersection of reinforcement learning, tactile sensing, and robotic construction. His most recognized contribution, "Robotic Architectural Assembly with Tactile Skills: Simulation and Optimization" (2021, 53 citations), established him as a leading voice in applying machine learning to autonomous architectural assembly — a field with profound implications for sustainable construction. Alongside this, his work on structured representations for robotic construction and the broader SL-Block modular system demonstrates a sustained commitment to combining combinatorial design with intelligent robotic execution. Belousov has made meaningful theoretical contributions as well, advancing optimal control through Hamilton-Jacobi-Bellman formulations and contextual reinforcement learning frameworks that enable robots to generalize skills across novel environments. His research into vision-based tactile sensors — spanning force estimation for teleoperation, active texture recognition, and manipulation exploration — reflects a growing focus on giving robots richer perceptual capabilities during contact-rich tasks. With over 110 cumulative citations across a relatively compact body of work, Belousov represents an emerging researcher whose interdisciplinary approach bridges abstract control theory, applied machine learning, and real-world robotic deployment in architecture and construction.