Resource Prediction for Humanoid Robots

TL;DR

This paper introduces a Markov chain-based prediction model for estimating future resource requirements of humanoid robots like ARMAR-III in dynamic human interaction scenarios, aiding resource management.

Contribution

It presents a novel resource prediction approach combining robot self-monitoring, environmental context, and online learning within the ArmarX framework.

Findings

Effective prediction of resource demands in real-time scenarios

Enhanced resource management for humanoid robots

Integration of environmental context improves accuracy

Abstract

Humanoid robots are designed to operate in human centered environments where they execute a multitude of challenging tasks, each differing in complexity, resource requirements, and execution time. In such highly dynamic surroundings it is desirable to anticipate upcoming situations in order to predict future resource requirements such as CPU or memory usage. Resource prediction information is essential for detecting upcoming resource bottlenecks or conflicts and can be used enhance resource negotiation processes or to perform speculative resource allocation. In this paper we present a prediction model based on Markov chains for predicting the behavior of the humanoid robot ARMAR-III in human robot interaction scenarios. Robot state information required by the prediction algorithm is gathered through self-monitoring and combined with environmental context information. Adding resource profiles allows generating probability distributions of possible future resource demands. Online learning of model parameters is made possible through disclosure mechanisms provided by the robot framework ArmarX.