Abstract
This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. 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 extracting object features that describe the object motions. The two modules are simultaneously trained 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 extracting features that distinguish the characteristics of object dynamics.
| Original language | English |
|---|---|
| Title of host publication | 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS |
| Pages | 1-7 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice Duration: 2008 Sep 22 → 2008 Sep 26 |
Other
| Other | 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS |
|---|---|
| City | Nice |
| Period | 08/9/22 → 08/9/26 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Active sensing based dynamical object feature extraction. / Nishide, Shun; Ogata, Tetsuya; Yokoya, Ryunosuke; Tani, Jun; Komatani, Kazunori; Okuno, Hiroshi G.
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2008. p. 1-7 4650794.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Active sensing based dynamical object feature extraction
AU - Nishide, Shun
AU - Ogata, Tetsuya
AU - Yokoya, Ryunosuke
AU - Tani, Jun
AU - Komatani, Kazunori
AU - Okuno, Hiroshi G.
PY - 2008
Y1 - 2008
N2 - This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. 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 extracting object features that describe the object motions. The two modules are simultaneously trained 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 extracting features that distinguish the characteristics of object dynamics.
AB - This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. 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 extracting object features that describe the object motions. The two modules are simultaneously trained 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 extracting features that distinguish the characteristics of object dynamics.
UR - http://www.scopus.com/inward/record.url?scp=69549110096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=69549110096&partnerID=8YFLogxK
U2 - 10.1109/IROS.2008.4650794
DO - 10.1109/IROS.2008.4650794
M3 - Conference contribution
SN - 9781424420582
SP - 1
EP - 7
BT - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
ER -