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About

Samuel Zapolsky is a robotics researcher whose work centers on rigid-body contact modeling, inverse dynamics control, and multi-body simulation for legged and manipulation systems. His research addresses some of the most challenging computational problems in robotics: accurately and efficiently modeling how robots interact with physical surfaces under friction and contact forces. Zapolsky's most influential contribution, "Fundamental Limitations in Performance and Interpretability of Common Planar Rigid-Body Contact Models" (2019, 19 citations), critically examines the assumptions underlying widely used contact models, a theme that runs throughout his career. His complementary 2017 work on data-efficient contact models demonstrates a forward-thinking hybrid approach, combining physics-inspired structure with machine learning to overcome the shortcomings of purely analytical methods. On the control side, his quadratic programming-based inverse dynamics framework for legged robots handling sticking and slipping contacts (2014, 12 citations) provides real-time capable solutions to a notoriously difficult problem, with earlier foundational work on slippery surface locomotion dating to 2013. He has also contributed to simulation methodology, exploring adaptive integration strategies that balance speed and accuracy, and to interactive robot design tools that accelerate the engineering process. Across roughly 78 total citations, Zapolsky's body of work offers both rigorous theoretical critique and practical algorithmic advances that continue to inform legged robotics and simulation research.