Papers
9
Total Citations
136
H-Index
6
About
Eric Price is a robotics and computer vision researcher whose work spans multi-robot coordination, aerial autonomy, and human and animal motion analysis. He is perhaps best known for developing SCRAM (Scalable Collision-avoiding Role Assignment with Minimal-Makespan), an influential algorithm for efficiently assigning interchangeable robots to spatial tasks while avoiding collisions — a contribution that has garnered nearly 60 citations across its conference and journal versions and remains a foundational reference in multi-robot task allocation. Price has also made significant strides in aerial robotics, particularly with autonomous airships: his work on deep residual reinforcement learning for blimp control and physics-grounded simulation of deformable airships in turbulent wind addresses a largely underexplored vehicle class, filling critical gaps in UAV research. His AirPose system, with 35 citations, demonstrates sophisticated multi-UAV fusion for markerless 3D human pose estimation in unstructured outdoor environments — pushing the frontier of real-world motion capture beyond laboratory constraints. More recently, Price has extended his vision-based methods to animal behaviour inference, offering scalable tools for ecological research. Across these domains, his research consistently emphasizes practical autonomy, scalability, and deployment in challenging real-world conditions.
Research Focus
Key Achievements
Top Papers
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- 3Deep Residual Reinforcement Learning based Autonomous Blimp Control16 citations · 2022
- 4Simulation and Control of Deformable Autonomous Airships in Turbulent Wind12 citations · 2022
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- 6Autonomous Blimp Control using Deep Reinforcement Learning8 citations · 2021
- 7Accelerated Video Annotation Driven by Deep Detector and Tracker4 citations · 2024
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