Papers
6
Total Citations
61
H-Index
3
About
Jilles Dibangoye is a leading researcher at the intersection of robotics, autonomous systems, and machine learning, with a focus on enabling intelligent vehicles and multi-robot teams to operate safely and efficiently in complex, dynamic environments. His work spans three core areas: learning from demonstration for autonomous driving, multi-robot exploration and coverage in 3D terrains, and neural-enhanced control for aerial robots. Dibangoye’s major contributions include pioneering a framework that models driver behavior from demonstrations using spatiotemporal lattices, a method that replaces costly hand-tuning with data-driven cost function learning (24 citations). He also developed a decentralized approach combining stochastic optimization and frontier-based methods for aerial multi-robot exploration, allowing fleets of drones to efficiently map unknown 3D environments (16 and 12 citations). More recently, he has advanced quadrotor control through neural-enhanced autopilots that adapt to complex nonlinear dynamics, and introduced LAPTNet-FPN, a multi-sensor fusion network for real-time semantic grid prediction critical for navigation and tracking. With a growing citation impact and a portfolio of innovative solutions, Dibangoye is shaping the future of autonomous robotics, making his work essential reading for students and researchers in intelligent systems.
Research Focus
Key Achievements
Top Papers
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- 4Neural Enhanced Control for Quadrotor Linear Behavior Fitting3 citations · 2022
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- 6Event-based neural learning for quadrotor control3 citations · 2023