Yorai Shaoul
Papers
2
Total Citations
6
H-Index
2
About
Yorai Shaoul is an emerging researcher specializing in multi-robot motion planning and autonomous manipulation systems. His work sits at the intersection of search-based planning, multi-agent coordination, and robotic manipulation, addressing one of robotics' most computationally demanding frontiers: orchestrating multiple robotic arms to work concurrently in shared environments. Shaoul's most notable contributions tackle the combinatorial complexity inherent in Multi-Robot-Arm Motion Planning (M-RAMP), where high-dimensional state spaces render traditional planning algorithms impractical. In his 2024 paper on accelerating search-based planning, he demonstrates how online-generated experiences can be leveraged to dramatically improve planning efficiency for multi-robot manipulation — a creative bridge between learning and classical search methods. Complementing this, his work on extending Conflict-Based Search (CBS) algorithms introduces principled ways to handle arbitrary constraints while maintaining bounded sub-optimality guarantees, advancing the state of the art in multi-agent coordination beyond standard grid-based assumptions. Though early in his publishing career, with his leading works accumulating citations within their debut year, Shaoul's research addresses problems of growing industrial and scientific relevance. His contributions position him as a promising voice in the robotics planning community, particularly as collaborative robot systems become increasingly central to automation and manufacturing.
Research Focus
Key Achievements
Top Papers
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