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About

Vladimir Ivan is a robotics researcher whose work spans motion planning, loco-manipulation, and robot perception, with particular emphasis on enabling robots to operate robustly in complex, real-world environments. His research has made significant contributions to both humanoid and quadruped robotics, addressing fundamental challenges in planning, control, and sensing. Ivan's early work established sophisticated topological and hierarchical representations for motion planning, enabling robots to solve intricate interaction problems such as wrapping motions that confound conventional approaches (2012, 2013). He advanced dynamic roadmap methods, culminating in the HDRM framework—proven resolution complete—for real-time planning in cluttered scenes (2017, 24 citations). His iDRM system brought real-time end-pose selection to humanoid robots in changing environments (2016, 33 citations), while complementary work scaled sampling-based planning to full floating-base systems. Beyond planning, Ivan developed warm-starting strategies for trajectory optimization (2018) and principled methods for learning motion constraints from demonstration (2017). His most-cited recent contribution, RoLoMa (2023, 57 citations), demonstrates robust loco-manipulation for legged robots with arms, directly tackling real-world deployment challenges including model uncertainty and sensor noise. Across over 280 combined citations, Ivan's portfolio reflects a coherent research vision: making capable, adaptable robots reliable enough for genuine deployment.