activity
20202025
most citedSelf-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

62 citations · 137 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2025

All Eyes, no IMU: Learning Flight Attitude from Vision Alone

Jesse J. Hagenaars, Stein Stroobants, Sander M. Bohte +1

Vision is an essential part of attitude control for many flying animals, some of which have no dedicated sense of gravity. Flying robots, on the other hand, typically depend heavil…

cs.RO2024

On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events

Jesse Hagenaars, Yilun Wu, Federico Paredes-Vallés +2

Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-s…

cs.RO2024

Direct learning of home vector direction for insect-inspired robot navigation

Michiel Firlefyn, Jesse Hagenaars, Guido de Croon

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 underlying this rema…

cs.RO2023★ 6 cited

Fully neuromorphic vision and control for autonomous drone flight

Federico Paredes-Vallés, Jesse Hagenaars, Julien Dupeyroux +3

Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based…

cs.NE2022★ 2 cited

Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks

J. Lu, J. J. Hagenaars, G. C. H. E. de Croon

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks with local information, making them suitable for online learning on n…

cs.CV2021★ 62 cited

Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

Jesse Hagenaars, Federico Paredes-Vallés, Guido de Croon

The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial…

cs.RO2020★ 49 cited

Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor

Julien Dupeyroux, Jesse Hagenaars, Federico Paredes-Vallés +1

Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, effi…

cs.RO2020★ 18 cited

Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs

J. J. Hagenaars, F. Paredes-Vallés, S. M. Bohté +1

Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the pro…