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pro vyhledávání: '"Glover, Arren"'
Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to the loss of informative points,
Externí odkaz:
http://arxiv.org/abs/2302.13796
Low latency and accuracy are fundamental requirements when vision is integrated in robots for high-speed interaction with targets, since they affect system reliability and stability. In such a scenario, the choice of the sensor and algorithms is impo
Externí odkaz:
http://arxiv.org/abs/2205.07657
There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance wh
Externí odkaz:
http://arxiv.org/abs/2105.11443
This paper investigates trajectory prediction for robotics, to improve the interaction of robots with moving targets, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be predicted with regression-based fittin
Externí odkaz:
http://arxiv.org/abs/2001.01248
The Asynchronous Time-based Image Sensor (ATIS) and the Spiking Neural Network Architecture (SpiNNaker) are both neuromorphic technologies that "unconventionally" use binary spikes to represent information. The ATIS produces spikes to represent the c
Externí odkaz:
http://arxiv.org/abs/1912.01320
Autor:
Vasco, Valentina, Glover, Arren, Mueggler, Elias, Scaramuzza, Davide, Natale, Lorenzo, Bartolozzi, Chiara
Publikováno v:
International Conference on Advanced Robotics (ICAR), 2017
Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, t
Externí odkaz:
http://arxiv.org/abs/1706.08713
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Autor:
Kreiser, Raphaela, Renner, Alpha, Leite, Vanessa R C, Serhan, Baris, Bartolozzi, Chiara, Glover, Arren, Sandamirskaya, Yulia
Publikováno v:
Frontiers in Neuroscience, 14
Frontiers in Neuroscience
Frontiers in Neuroscience, Vol 14 (2020)
Frontiers in Neuroscience
Frontiers in Neuroscience, Vol 14 (2020)
In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::596767646d42570f1ea06ed7309fc524
https://hdl.handle.net/20.500.11850/426223
https://hdl.handle.net/20.500.11850/426223