Experience-based imitation using RNNPB

Ryunosuke Yokoya*, Tetsuya Ogata, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. We considered the target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object's motion and its own arm motion. For generalization, we applied the RNNPB (recurrent neural network with parametric bias) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object's motion presented by a human operator. Experimental results proved the generalization capability of our method, which enables the robot to imitate not only motion it has experienced, but also unknown motion through nonlinear combination of the experienced motions.

Original languageEnglish
Pages (from-to)1351-1367
Number of pages17
JournalAdvanced Robotics
Issue number12
Publication statusPublished - 2007 Dec 1
Externally publishedYes


  • Active sensing
  • Humanoid robot
  • Imitation
  • Recurrent neural network

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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