Daniel Nakhimovich
Papers
4
Total Citations
59
H-Index
3
About
Daniel Nakhimovich is a robotics researcher whose work lies at the intersection of algorithmic manipulation and topological data analysis. His primary contributions focus on solving the complex geometric and combinatorial challenges inherent in object rearrangement—a critical capability for robots operating in cluttered, real-world environments. Nakhimovich’s most influential work, “Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search” (32 citations), introduces novel algorithmic structures that enable robots to efficiently rearrange uniform objects by carefully navigating geometric constraints and avoiding collisions. He further pushes the boundaries of non-prehensile manipulation in his highly-cited 2022 paper (19 citations), where he pioneers the use of persistent homology—a topological tool—to guide robots in selecting effective pushing actions to clear constrained workspaces. This innovative application of topology to robotics demonstrates Nakhimovich’s ability to bridge abstract mathematical concepts with practical manipulation tasks. Additionally, he has contributed to the broader discourse on robotics for disaster response, co-authoring a chapter on the promises and pitfalls of using robots as enablers of resiliency. Through his work, Nakhimovich is advancing the fundamental algorithms that will allow robots to intelligently and autonomously organize their environments.
Research Focus
Key Achievements
Top Papers
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- 2Persistent Homology for Effective Non-Prehensile Manipulation19 citations · 2022
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