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 - 2008/12/1
Y1 - 2008/12/1
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
AN - SCOPUS:69549110096
SN - 9781424420582
T3 - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
SP - 1
EP - 7
BT - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
T2 - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Y2 - 22 September 2008 through 26 September 2008
ER -