Titlebook: Advances in Physical Agents II; Proceedings of the 2 Luis M. Bergasa,Manuel Oca?a,Pedro Revenga Conference proceedings 2021 The Editor(s) (

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Koichiro Sugihara,Naohiro Hayashibaraobjects of interest to a map of the environment as soon as the objects are detected by the vision system of the robot. However, these maps are usually static, and do not take into account the dynamic nature of a home, where anyone could move an object after the robot has seen it. In this paper, we p

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Adaptive optical-phase equalizerches based on DQN agents will be studied. The DQN agent learn a policy (set of actions) for lane following tasks using visual and driving features obtained from sensors onboard the vehicle and a model-based path planner. The policy objective is to drive as fast as possible following the center of th

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Studies in Computational Intelligenceove the customer experience. Federated learning is a popular framework that allows multiple distributed devices to train deep learning models remotely, collaboratively, and preserving data privacy. However, little research has been done regarding the scenario where data distribution is non-identical

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Adaptive optical-phase equalizers. Moreover, with the advance of service robotics and its increasing use in all kinds of real-world applications, nowadays, robots are expected to operate well in unstructured and dynamic environments, where for instance they could work close to human counterparts. In this context, traditional navig

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https://doi.org/10.1007/978-3-540-33457-6role in it. This is especially important for robots that interact with people in household, eldercare or industrial collaborative scenarios. We have proposed in earlier works an RCA called CORTEX designed for social robots operating in HRI environments. One of CORTEX’s main elements is a working mem

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https://doi.org/10.1007/978-3-540-33457-6y to keep the digital twins update and have the capability to control them. The design of the corresponding models is quite a challenge. In this work, we show how to achieve this with simple tools through an example with educational purposes. We also show how the model handles much more sophisticate

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