The design of robots that interact autonomously with the environment and exhibit complex
behaviours is an open challenge that can benefit from understanding what makes living …

Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

Training deep neural networks at the edge on light computational devices, embedded
systems and robotic platforms is nowadays very challenging. Continual learning techniques …

Robotic vision is a field where continual learning can play a significant role. An embodied
agent operating in a complex environment subject to frequent and unpredictable changes is …

In the last few years, we have witnessed a renewed and fast-growing interest in continual
learning with deep neural networks with the shared objective of making current AI systems …

Deployed into an open world, object detectors are prone to open-set errors, false positive
detections of object classes not present in the training dataset. We propose GMM-Det, a real …

In object detection, false negatives arise when a detector fails to detect a target object. To
understand why object detectors produce false negatives, we identify five 'false negative …

Object recognition in real-world environments is one of the fundamental and key tasks in
computer vision and robotics communities. With the advanced sensing technologies and low …

The impressive performance of deep learning architectures is associated with a massive
increase in model complexity. Millions of parameters need to be tuned, with training and …