no code implementations • 18 Feb 2024 • Matthew Yedutenko, Federico Paredes-Valles, Lyes Khacef, Guido C. H. E. de Croon
Using synthetic data we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise.
no code implementations • 15 Nov 2023 • Alessandro Mancinelli, Bart D. W. Remes, Guido C. H. E. de Croon, Ewoud J. J. Smeur
Overactuated Tilt Rotor Unmanned Aerial Vehicles are renowned for exceptional wind resistance and a broad operational range, which poses complex control challenges due to non-affine dynamics.
no code implementations • 22 May 2023 • Dario Izzo, Emmanuel Blazquez, Robin Ferede, Sebastien Origer, Christophe De Wagter, Guido C. H. E. de Croon
Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission.
no code implementations • 18 Apr 2023 • Stein Stroobants, Christophe De Wagter, Guido C. H. E. de Croon
In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks.
no code implementations • ICCV 2023 • Federico Paredes-Vallés, Kirk Y. W. Scheper, Christophe De Wagter, Guido C. H. E. de Croon
Event cameras have recently gained significant traction since they open up new avenues for low-latency and low-power solutions to complex computer vision problems.
1 code implementation • 24 Nov 2022 • YiLun Wu, Federico Paredes-Vallés, Guido C. H. E. de Croon
Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, rendering them unsuitable for robotic applications with limited compute and energy budget.
no code implementations • 14 Sep 2022 • Rik J. Bouwmeester, Federico Paredes-Vallés, Guido C. H. E. de Croon
In this work, we present NanoFlowNet, a lightweight convolutional neural network for real-time dense optical flow estimation on edge computing hardware.
1 code implementation • 30 Aug 2022 • Yingfu Xu, Guido C. H. E. de Croon
Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world.
no code implementations • 11 May 2022 • Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof, Srivatsan Krishnan, Colby Banbury, Mark Mazumder, Shvetank Prakash, Jason Jabbour, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi
Machine learning (ML) has become a pervasive tool across computing systems.
1 code implementation • 28 Mar 2022 • Cheng Liu, Erik-Jan van Kampen, Guido C. H. E. de Croon
Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones.
no code implementations • 29 Jun 2021 • Nitin J. Sanket, Chahat Deep Singh, Chethan M. Parameshwara, Cornelia Fermüller, Guido C. H. E. de Croon, Yiannis Aloimonos
Our network can detect propellers at a rate of 85. 1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget.
no code implementations • CVPR 2021 • Federico Paredes-Valles, Guido C. H. E. de Croon
In this work we approach, for the first time, the intensity reconstruction problem from a self-supervised learning perspective.
1 code implementation • 9 Mar 2021 • Mario Coppola, Jian Guo, Eberhard Gill, Guido C. H. E. de Croon
The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i. e., transitions in observations from the perspective of a single robot in the swarm) given a policy.
1 code implementation • 5 Mar 2021 • Daniël Willemsen, Mario Coppola, Guido C. H. E. de Croon
MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC).
2 code implementations • 6 Jan 2021 • Yingfu Xu, Guido C. H. E. de Croon
In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur.
1 code implementation • 12 Mar 2020 • Shushuai Li, Mario Coppola, Christophe De Wagter, Guido C. H. E. de Croon
Accurate relative localization is an important requirement for a swarm of robots, especially when performing a cooperative task.
Robotics Multiagent Systems
no code implementations • 16 Dec 2019 • Kirk Y. W. Scheper, Guido C. H. E. de Croon
Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics.
1 code implementation • 25 Sep 2019 • Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.
no code implementations • ICCV 2019 • Tom van Dijk, Guido C. H. E. de Croon
We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so.
1 code implementation • 28 Jul 2018 • Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon
Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer.
no code implementations • 18 Apr 2018 • Mario Coppola, Jian Guo, Eberhard K. A. Gill, Guido C. H. E. de Croon
We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern.
Robotics
no code implementations • 31 Jan 2017 • Bas J. Pijnacker Hordijk, Kirk Y. W. Scheper, Guido C. H. E. de Croon
In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem.