Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes

TL;DR

This paper introduces DPMPB, a deep predictive modeling approach with parametric bias, designed to adaptively handle complex and changing relationships in robotic tasks, mimicking human-like adaptability.

Contribution

The paper proposes DPMPB, a novel deep predictive model that effectively manages modeling difficulties and temporal changes in robotic systems.

Findings

DPMPB successfully models complex relationships in robot tasks.

DPMPB adapts to temporal changes in models.

Experimental results demonstrate DPMPB's effectiveness.

Abstract

When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes of the model. In this study, we have developed Deep Predictive Model with Parametric Bias (DPMPB) as a more human-like adaptive intelligence to deal with these modeling difficulties and temporal model changes. We categorize and summarize the theory of DPMPB and various task experiments on the actual robots, and discuss the effectiveness of DPMPB.