Papers
8
Total Citations
106
H-Index
6
About
Jakub Lengiewicz is a computational researcher specializing in modular robotics, programmable matter, and distributed autonomous systems. His work focuses on the fundamental challenge of enabling large ensembles of simple robotic modules to collectively reconfigure, locomote, and generate mechanical force at scale — a frontier problem at the intersection of robotics, mechanics, and distributed computing. Lengiewicz's most influential contribution, "Efficient Collective Shape Shifting and Locomotion of Massively-Modular Robotic Structures" (2018, 30 citations), introduced an elegant divide-and-conquer methodology in which modules are partitioned into fixed structural frames and mobile units that flow through them — a concept that significantly advances the practicality of large-scale shape-shifting robots. His complementary work on scalable collective actuation (2014–2015) addressed how force output can grow proportionally with module count, tackling one of programmable matter's core engineering challenges. A recurring theme in his research is distributing intelligence across the robotic ensemble itself. His 2021 paper on predicting unsafe reconfigurations (25 citations) exemplifies this, enabling robots to autonomously assess structural failure risks without centralized control. With work spanning localization algorithms, conjugate gradient solvers, and mechanical force computation, Lengiewicz has built a cohesive and rigorous body of research shaping the theoretical foundations of tomorrow's programmable matter systems.
Research Focus
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Top Papers
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- 3Modular-robotic structures for scalable collective actuation13 citations · 2015
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