Sanket Salunkhe is a robotics researcher and doctoral student whose work focuses on building autonomous aerial and multi-robot systems that can perceive, reason, and act reliably in complex environments. At the NYU Agile Robotics and Perception Lab, he contributes to UAV autonomy research spanning perception, state estimation, planning, and deployment on real robotic platforms.
His research brings together visual-inertial odometry and SLAM, motion and trajectory planning, control, and machine learning. He is particularly interested in systems that bridge rigorous algorithmic development with field-ready autonomy: aerial robots that can localize, navigate, and collaborate safely when sensing, dynamics, and communication are uncertain.
Recent work includes mixed-reality interfaces for intuitive human-drone collaboration, real-time trajectory planning and control for fixed-wing aerial systems, and federated Gaussian-process learning for scalable multi-robot modelling. These projects reflect a broader goal: creating robotic systems that are adaptive, interpretable, and useful beyond controlled laboratory settings.
Research interests
- Autonomous aerial vehicles and multi-robot systems
- State estimation, visual-inertial SLAM, and perception
- Motion planning, trajectory optimization, and control
- Distributed learning and probabilistic modelling
- Human-robot interaction and mixed-reality interfaces
Before joining NYU, Sanket developed hands-on experience across robotics product development and autonomous vehicles, including mechanical design, embedded integration, computer vision, and deep learning. This systems perspective continues to shape his approach to robotics research—from algorithm design through deployment and evaluation.