A Dynamic Neural Network Approach to Generating Robot's Novel Actions: A Simulation Experiment

TL;DR

This paper presents a dynamic neural network model enabling robots to learn basic actions and generate novel, creative actions through self-organization, with potential applications in interactive human-robot scenarios.

Contribution

It introduces a novel neural network approach that allows robots to produce creative actions by modulating learned behaviors, inspired by computational creativity principles.

Findings

The model successfully learned basic actions and generated novel ones.

Neural activity analysis showed self-organization of memory structures.

Different learning methods led to varied creative behaviors.

Abstract

In this study, we investigate how a robot can generate novel and creative actions from its own experience of learning basic actions. Inspired by a machine learning approach to computational creativity, we propose a dynamic neural network model that can learn and generate robot's actions. We conducted a set of simulation experiments with a humanoid robot. The results showed that the proposed model was able to learn the basic actions and also to generate novel actions by modulating and combining those learned actions. The analysis on the neural activities illustrated that the ability to generate creative actions emerged from the model's nonlinear memory structure self-organized during training. The results also showed that the different way of learning the basic actions induced the self-organization of the memory structure with the different characteristics, resulting in the generation of different levels of creative actions. Our approach can be utilized in human-robot interaction in which a user can interactively explore the robot's memory to control its behavior and also discover other novel actions.