In this paper, we propose an online algorithm for multimodal categorization based on the autonomously acquired multimodal information and partial words given by human users. For multimodal concept formation, multimodal latent Dirichlet allocation (MLDA) using Gibbs sampling is extended to an online version. We introduce a particle filter, which significantly improve the performance of the online MLDA, to keep tracking good models among various models with different parameters. We also introduce an unsupervised word segmentation method based on hierarchical Pitman-Yor Language Model (HPYLM). Since the HPYLM requires no predefined lexicon, we can make the robot system that learns concepts and words in completely unsupervised manner. The proposed algorithms are implemented on a real robot and tested using real everyday objects to show the validity of the proposed system.
A sensor attached to the sole of the foot of a biped robot to detect a Zero Moment Point (ZMP) is proposed and basic experimental results are presented. The sensor simultaneously detects the center position of a two-dimensional distributed load on the surface of the sensor and the total load of the distribution.The sensor is sheet-like in form, lightweight (0.2g/cm 2 ), offers a high-speed response (within 1 ms), and needs a little wiring (four wires). In the present paper, the principle of the above-described sensor and its characteristics are described, and the ZMP locus is shown for a walking biped robot with the attached sensors.
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