Prediction of object manipulation using tactile sensor information by a humanoid robot

Abstract

This paper presents a framework of lifting-up manipulation acquisition based on tactile sensing information by a humanoid robot. Feature extraction from sensor information, including tactile information, is presented using linear and nonlinear mappings. Information acquired from sensors is mapped to a lower-dimensional space for predicting success of lifting-up task. Robot judges success or failure of the manipulation using the obtained feature space and object orientation. The proposed method was evaluated by simulation with a humanoid robot. Sensor information obtained at the beginning stage of lifting-up task was utilized to predict whether the robot can accomplish the task without dropping down the object. It was verified that the proposed feature extraction provides sufficient information to predict success of the task. The prediction will be utilized to modify posture of the robot.