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  • Navigation test
  • Object recognition & manipulation

  • Human robot communication based on hierarchical spatial concept
  • Human life support based on spatial concept

  • Object manipulation based on spacial concept
  • Transfer learning of spatial concept

  • Spacial concept learning
  • Service example based on spacial concept

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  • Experimental room 10 (Home environment)
    The data sets were collected for learning spatial concept. The data sets contain the robot's position estimated by Monte Carlo Localization, and image information captured by a Web camera at each position.

Open source software

  • Spacial Concept Formation
    This is a model to learn spatial concept from images and robot position information. Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on the basis of the information obtained from their sensors, e.g., laser sensors, microphones, and cameras, and recognize a variety of objects, places, and situations in an ambient environment. Above all, we consider it important for the robot to learn the names that humans associate with places in the environment and the spatial areas corresponding to these names[ 1 ].
  • Nonparametric Bayesian Double Articulation Analyzer (NPB-DAA)
    This is a Python implementation for Nonparametric Bayesian Double Articulation Analyzer (NPB-DAA). The NPB-DAA can directly acquire language and acoustic models from observed continuous speech signals.

    This generative model is called hierarchical Dirichlet process hidden language model (HDP-HLM), which is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM.


 [ 1 ] Akira Taniguchi, Tadahiro Taniguchi, Tetsunari Inamura,Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences,IEEE Transactions on Cognitive and Developmental Systems, Vol.8 (4), pp. 285-297 .(2016)DOI: 10.1109/TCDS.2016.2565542