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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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樓主: 鳴叫大步走
31#
發(fā)表于 2025-3-27 00:56:36 | 只看該作者
32#
發(fā)表于 2025-3-27 02:35:32 | 只看該作者
Adaptive optical-phase equalizer personal spaces of the people surrounding them, or not getting in the way of human-to-human and human-to-object interactions. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so
33#
發(fā)表于 2025-3-27 08:06:37 | 只看該作者
https://doi.org/10.1007/978-3-540-33457-6 datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task.
34#
發(fā)表于 2025-3-27 09:49:33 | 只看該作者
35#
發(fā)表于 2025-3-27 17:13:46 | 只看該作者
https://doi.org/10.1007/978-3-540-33457-6rty conversation is a way of improving a robot’s naturalness in human-robot interaction. A system based on a competitive neural network can decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli are produced. One important aspect in this process
36#
發(fā)表于 2025-3-27 18:17:19 | 只看該作者
37#
發(fā)表于 2025-3-28 00:55:21 | 只看該作者
38#
發(fā)表于 2025-3-28 04:43:41 | 只看該作者
https://doi.org/10.1007/978-3-030-62579-5Active-aware navigation; Long-term object localization; Convolutional Neural Network Models; Autonomous
39#
發(fā)表于 2025-3-28 09:21:23 | 只看該作者
40#
發(fā)表于 2025-3-28 13:49:35 | 只看該作者
Lifelong Object Localization in Robotic ApplicationsOverview:
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