Analysis of Reliable Predictability based Motion Generation using RNNPB

Abstract

Reliable predictability, which is tightly connected to consistency of environmental changes, is one of the main factors that determine human behaviors. As a constructive approach to understanding this mechanism, the authors have developed a method to generate autonomous object pushing motions based on consistency of object motions using a humanoid robot. The method consists of constructing a dynamics prediction model using Recurrent Neural Network with Parametric Bias (RNNPB), and motion searching based on an object consistency evaluation function using Steepest Descent Method. First, RNNPB is trained using the observed object dynamics and robot motion sequences, acquired during active sensing with objects. Next, the Steepest Descent Method is applied for searching the reliably predictable motion through the constructed dynamics model. Finally, the obtained motion is linked to the initial object image using a hierarchical neural network. The model inputs the object image outputting the reliably predictable robot motion which induces consistent object motions. The model was analyzed through two experiments, pushing cylindrical objects with a humanoid robot. The analysis has shown the method's effectivity and limitations to generate consistent object motions. I. I NTRODUCTION