TempFuser: Learning Agile, Tactical, and Acrobatic Flight Maneuvers Using a Long Short-Term Temporal Fusion Transformer
Hyunki Seong, David Hyunchul Shim

TL;DR
TempFuser is a novel transformer-based architecture that learns agile, tactical, and acrobatic flight maneuvers for aerial dogfighting, integrating long-term tactics and short-term agility to outperform baseline models in complex simulations.
Contribution
Introduces TempFuser, a long short-term temporal fusion transformer that captures both strategic and agile flight maneuvers in dogfight scenarios, enabling robust and human-like aerial tactics.
Findings
Outperforms baseline policies in complex dogfight simulations
Exhibits human-like acrobatic maneuvers against superior opponents
Demonstrates robustness in supersonic and low-altitude pursuits
Abstract
Dogfighting is a challenging scenario in aerial applications that requires a comprehensive understanding of both strategic maneuvers and the aerodynamics of agile aircraft. The aerial agent needs to not only understand tactically evolving maneuvers of fighter jets from a long-term perspective but also react to rapidly changing aerodynamics of aircraft from a short-term viewpoint. In this paper, we introduce TempFuser, a novel long short-term temporal fusion transformer architecture that can learn agile, tactical, and acrobatic flight maneuvers in complex dogfight problems. Our approach integrates two distinct temporal transition embeddings into a transformer-based network to comprehensively capture both the long-term tactics and short-term agility of aerial agents. By incorporating these perspectives, our policy network generates end-to-end flight commands that secure dominant positions…
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Taxonomy
TopicsGuidance and Control Systems · Human Pose and Action Recognition · Aerospace and Aviation Technology
