Abstract
This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.
| Original language | English |
|---|---|
| Pages (from-to) | 2035-2057 |
| Number of pages | 23 |
| Journal | Advanced Robotics |
| Volume | 23 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - 2009 Oct 1 |
| Externally published | Yes |
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Keywords
- Active sensing
- Affordance
- Bidirectional training
- Humanoid robots
- Neural networks
ASJC Scopus subject areas
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Hardware and Architecture
- Software
Cite this
Self-organization of dynamic object features based on bidirectional training. / Nishide, Shun; Ogata, Tetsuya; Tani, Jun; Komatani, Kazunori; Okuno, Hiroshi G.
In: Advanced Robotics, Vol. 23, No. 15, 01.10.2009, p. 2035-2057.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Self-organization of dynamic object features based on bidirectional training
AU - Nishide, Shun
AU - Ogata, Tetsuya
AU - Tani, Jun
AU - Komatani, Kazunori
AU - Okuno, Hiroshi G.
PY - 2009/10/1
Y1 - 2009/10/1
N2 - This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.
AB - This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.
KW - Active sensing
KW - Affordance
KW - Bidirectional training
KW - Humanoid robots
KW - Neural networks
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UR - http://www.scopus.com/inward/citedby.url?scp=70449589606&partnerID=8YFLogxK
U2 - 10.1163/016918609X12529289797027
DO - 10.1163/016918609X12529289797027
M3 - Article
VL - 23
SP - 2035
EP - 2057
JO - Advanced Robotics
JF - Advanced Robotics
SN - 0169-1864
IS - 15
ER -