Andrew Tinka

Amazon (United States), University of California, Berkeley

Papers

Total Citations

234

H-Index

About

Andrew Tinka is a leading researcher at the intersection of artificial intelligence and robotics, with a primary focus on multi-agent path finding (MAPF) and its real-world deployment. His most influential work tackles the critical gap between theoretical MAPF algorithms and practical, robust execution in environments like automated warehouses. In his highly cited 2019 paper (129 citations), Tinka addressed the challenge of persistently and robustly executing MAPF schedules over long time horizons, moving beyond simplified agent assumptions to handle the complexities of physical robots. He further advanced the field by integrating task assignment with collision-free path planning, extending the popular Conflict-Based Search (CBS) framework to optimize both agent routing and task allocation simultaneously (91 citations). Earlier in his career, Tinka demonstrated a unique breadth by designing a network of robotic Lagrangian sensors for real-time environmental monitoring in shallow waters, showcasing his ability to apply robotic systems to diverse domains. His work is distinguished by its direct impact on industrial automation, providing foundational solutions that enable scalable, reliable multi-robot coordination in high-stakes logistics environments.