72 citations · 274 across the 20 of their papers we have counts for
22 papers
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof +8
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the…
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…
An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System
Diana A. Olejnik, Sunyi Wang, Julien Dupeyroux +4
This paper discusses a low-cost, open-source and open-hardware design and performance evaluation of a low-speed, multi-fan wind system dedicated to micro air vehicle (MAV) testing.…
Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp
Marina González-Álvarez, Julien Dupeyroux, Federico Corradi +1
Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge r…
The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
Christophe De Wagter, Federico Paredes-Vallés, Nilay Sheth +1
Robotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space wit…
Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors
Stein Stroobants, Julien Dupeyroux, Guido de Croon
The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous…
Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments
Bardienus P. Duisterhof, Shushuai Li, Javier Burgués +2
Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make G…
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes
Benjamin Keltjens, Tom van Dijk, Guido de Croon
Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as K…
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…
A model-based framework for learning transparent swarm behaviors
Mario Coppola, Jian Guo, Eberhard Gill +1
This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the auto…
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models
Daniël Willemsen, Mario Coppola, Guido C. H. E. de Croon
Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency. This research…
A toolbox for neuromorphic sensing in robotics
Julien Dupeyroux, Stein Stroobants, Guido de Croon
The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the…
FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow
Raoul Dinaux, Nikhil Wessendorp, Julien Dupeyroux +1
Course estimation is a key component for the development of autonomous navigation systems for robots. While state-of-the-art methods widely use visual-based algorithms, it is worth…
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…
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
F. Paredes-Vallés, G. C. H. E. de Croon
Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events…
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz +5
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable infere…
How do neural networks see depth in single images?
Tom van Dijk, Guido C. H. E. de Croon
Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a public…
Autonomous drone race: A computationally efficient vision-based navigation and control strategy
S. Li, M. M. O. I. Ozo, C. De Wagter +1
Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defin…
A Comparative Study of Bug Algorithms for Robot Navigation
Kimberly McGuire, Guido de Croon, Karl Tuyls
This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these meth…
Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning
Mario Coppola, Jian Guo, Eberhard K. A. Gill +1
In this work, we explore emergent behaviors by swarms of anonymous, homogeneous, non-communicating, reactive robots that do not know their global position and have limited relative…
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
Diogo Martins, Kevin van Hecke, Guido de Croon
We study how autonomous robots can learn by themselves to improve their depth estimation capability. In particular, we investigate a self-supervised learning setup in which stereo…
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
G. C. H. E. de Croon
Self-supervised learning (SSL) is a reliable learning mechanism in which a robot enhances its perceptual capabilities. Typically, in SSL a trusted, primary sensor cue provides supe…