Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model
作者： Akira Taniguchi , Yoshinobu Hagiwara , Tadahiro Taniguchi , Tetsunari Inamura
DOI: 10.1080/01691864.2020.1817777
关键词: Probabilistic inference 、 Bayesian probability 、 Motion planning 、 Reinforcement learning 、 Maximization 、 Artificial intelligence 、 Computer science 、 Mobile robot 、 Generative model 、 Trajectory 、 Robot
摘要: Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multimodal place category acquired from the robot's spatial experience including vision, speech-language, and self-position. The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as 'Go to the kitchen', via probabilistic inference on a Bayesian generative model using spatial concepts. Specifically …
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