Our work on online, on-board learning of depth from events has been accepted at CVPR 2025. Event cameras provide low-latency perception for only milliwatts of …
Category: Research
We introduce a novel obstacle avoidance pipeline named Memory-Augmented Varying-speed Reinforcement Learning (MAVRL). MAVRL utilizes depth maps, along with the drone and target’s states as …
Inspired by how ants visually recognize their environment and count their steps to navigate back home, MAVLab researchers have developed an insect-inspired navigation strategy for …
In collaboration with ESA’s Advanced Concepts Team, we published our work on end-to-end guidance and control networks in the June 2024 issue of Science Robotics. …
Our work on fully neuromorphic vision and control for autonomous drone flight has been published in the Science Robotics May 2024 issue. In this work, …
Abstract Insects have long been recognized for their ability to navigate and return home using visual cues from their nest’s environment. However, the precise mechanism …
The work from PhD candidate Liming Zheng and Assistant Professor Salua Hamaza from the BioMorphic Intelligence Lab was published in the journal of IEEE Robotics and Automation …
With an escalating demand for quadcopters in emergency response, inspection and delivery, the need for speed and energy efficiency is paramount. However, developing autonomous systems …
Birds display remarkable adaptability in harnessing their surroundings for efficient flight, exemplified by the ingenious behavior observed in Kestrels. These birds employ a unique technique …
Federico Paredes-Vallés, Kirk W. Y. Scheper, Christophe De Wagter, Guido C. H. E. de Croon Event cameras have recently gained significant traction …