Human-robot cooperation using quasi-symbols generated by RNNPB model

Tetsuya Ogata, Shohei Matsumoto, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

We describe a means of human robot interaction based not on natural language but on "quasi symbols," which represent sensory-motor dynamics in the task and/or environment. It thus overcomes a key problem of using natural language for human-robot interaction - the need to understand the dynamic context The quasi-symbols used are motion primitives corresponding to the attractor dynamics of the sensory-motor flow. These primitives are extracted from the observed data using the recurrent neural network with parametric bias (RNNPB) model. Binary representations based on the model parameters were implemented as quasi symbols in a humanoid robot, Robovie. The experiment task was robot-arm operation on a table. The quasi-symbols acquired by learning enabled the robot to perform novel motions. A person was able to control the arm through speech interaction using these quasi-symbols. These quasi symbols formed a hierarchical structure corresponding to the number of nodes in the model. The meaning of some of the quasi-symbols depended on the context, indicating that they are useful for human-robot interaction.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Pages2156-2161
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Robotics and Automation, ICRA'07 - Rome
Duration: 2007 Apr 102007 Apr 14

Other

Other2007 IEEE International Conference on Robotics and Automation, ICRA'07
CityRome
Period07/4/1007/4/14

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ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

Cite this

Ogata, T., Matsumoto, S., Tani, J., Komatani, K., & Okuno, H. G. (2007). Human-robot cooperation using quasi-symbols generated by RNNPB model. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2156-2161). [4209404] https://doi.org/10.1109/ROBOT.2007.363640