Predicting object dynamics from visual images through active sensing experiences

Shun Nishide*, Tetsuya Ogata, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects relative to the robot's motion from visual images. During the training phase, the authors use the recurrent neural network with parametric bias (RNNPB) to self-organize the dynamics of objects manipulated by the robot into the PB space. The acquired PB values, static images of objects and robot motor values are input into a hierarchical neural network to link the images to dynamic features (PB values). The neural network extracts prominent features that each induce object dynamics. For prediction of the motion sequence of an unknown object, the static image of the object and robot motor value are input into the neural network to calculate the PB values. By inputting the PB values into the closed loop RNNPB, the predicted movements of the object relative to the robot motion are calculated recursively. Experiments were conducted with the humanoid robot Robovie-IIs pushing objects at different heights. The results of the experiment predicting the dynamics of target objects proved that the technique is efficient for predicting the dynamics of the objects.

Original languageEnglish
Pages (from-to)527-546
Number of pages20
JournalAdvanced Robotics
Issue number5
Publication statusPublished - 2008 Apr 1
Externally publishedYes


  • Active sensing
  • Dynamics
  • Humanoid robot
  • Neural networks
  • Object manipulation

ASJC Scopus subject areas

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


Dive into the research topics of 'Predicting object dynamics from visual images through active sensing experiences'. Together they form a unique fingerprint.

Cite this