Active sensing based dynamical object feature extraction

— 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.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Shun Nishide, Tetsuya Ogata, Ryunosuke Yokoya, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno
• Keywords: Dynamics Learning Module, Feature Extraction Module, IROS 2008, Object Motions, Robotics