PhD · Aerospace Engineering
Amin
Yazdanshenas
I build intelligent autonomy frameworks that integrate learning, perception, planning, and control for robotic systems — with particular emphasis on aerial robotics and embodied AI.
Education
Capabilities
Skills
Programming
Simulation & AI
Research
Featured Projects
View all →GateNet — Vision-Based Gate Segmentation for Drone Racing
A compact U-Net that replaces privileged simulator segmentation with RGB-only gate prediction, closing the sim-to-real perception gap for DreamerV3 drone racing. Achieves 0.93 mean IoU and 1.0 median IoU on held-out validation.
Isaac Drone Racer 2
Modernized autonomous drone racing RL framework for Isaac Sim 5.1 and Isaac Lab 2.3.2. Supports vision-based gate tracking, semantic segmentation, swarm racing with multiple agents, and multi-objective reward functions for high-speed navigation.
Adaptive Quaternion Sliding Mode Control for Nano Quadrotors
A globally stable, computationally efficient adaptive SMC framework for nano quadrotors. Validated in 130+ hardware flight trials on a 32 g Crazyflie, enabling flip maneuvers and 3g accelerations.