Papers
3
Total Citations
7
H-Index
2
About
Pierre Susbielle is a robotics researcher whose work bridges the gap between complex aerial dynamics and practical, real-world autonomy. His primary research areas include quadrotor control, neural-enhanced autopilot design, and teleoperation systems for situational awareness. Susbielle’s major contributions lie in developing intelligent control frameworks that simplify the traditionally tedious process of tuning quadrotor autopilots. In his highly cited work, “Neural Enhanced Control for Quadrotor Linear Behavior Fitting” (2022, 3 citations) and “Event-based neural learning for quadrotor control” (2023, 3 citations), he tackles the challenge of nonlinear dynamics—such as battery discharge, blade flapping, and gyroscopic effects—by integrating neural networks to achieve efficient, adaptive flight control. More recently, his 2025 paper on “Real-time Photorealistic Mapping for Situational Awareness in Robot Teleoperation” (1 citation) addresses a critical bottleneck in remote exploration: enabling teleoperators to rapidly build an intuitive understanding of unknown environments through online 3D mapping. Susbielle’s work is notable for its practical impact, offering streamlined solutions that reduce development time while enhancing robot performance. His research is particularly valuable for students and engineers seeking to deploy robust, learning-based control in field robotics.
Research Focus
Key Achievements
Top Papers
- 1Neural Enhanced Control for Quadrotor Linear Behavior Fitting3 citations · 2022
- 2Event-based neural learning for quadrotor control3 citations · 2023
- 3