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About

Alexander Shkolnik is a leading roboticist whose work bridges the gap between high-dimensional motion planning and dynamic, real-world locomotion. His primary research areas include kinodynamic planning, legged locomotion, and manipulation in high-dimensional configuration spaces. Shkolnik is best known for his pioneering work on the LittleDog quadruped robot, where he developed algorithms enabling robust bounding and dynamic motions over rough terrain—a significant departure from quasi-static walking gaits. His 2010 paper on bounding with LittleDog has garnered 126 citations, reflecting its foundational impact on dynamic legged robotics. He also made key contributions to asymptotically-optimal sampling-based planning for manipulation, with two related papers accumulating over 100 citations combined. Notably, Shkolnik advanced the use of task-space Voronoi bias to efficiently plan in configuration spaces exceeding 1,000 dimensions, a critical breakthrough for high-DOF manipulators. Earlier in his career, he explored the intersection of robotics and neuroscience with the HYBROTS project, studying embodied cultured neural networks. Shkolnik’s work is essential reading for anyone interested in enabling robots to move with speed, agility, and intelligence in complex, unstructured environments.