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On this basis, we propose a novel search strategy to improve the search efficiency of mobile robot at the scale of the entire home environment.
Proceedings ArticleDOI
09 Jul 2007
11 Citations
Any robot search scheme must then necessarily involve excess movement and/or increased component complexity (e. g., memory or communication).
The experimental results demonstrated that the robot was able to more accurately learn the relationships between words and the place in the environmental map incrementally by using the proposed method.
Thus there is a developmental difference in children’s potential to learn words from a robot.
Since the HPYLM requires no predefined lexicon, we can make the robot system that learns concepts and words in completely unsupervised manner.
Furthermore, we propose a method that allows a robot to effectively use the learned words and their meanings for self-localization tasks.
The algorithm is tested with Stanford robot in simulation, and the result shows that the presented method has higher search efficiency and can obtain better solution in comparison with the existing methods.
In this paper we propose LDA-based framework for multimodal categorization and words grounding for robots.