Resource Prediction for Humanoid Robots
This work addresses resource management challenges for humanoid robots in interactive scenarios, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of predicting future resource requirements like CPU or memory usage for humanoid robots in dynamic human-centered environments, presenting a Markov chain-based model that generates probability distributions of resource demands by combining robot state and environmental context, with results enabling online learning through the ArmarX framework.
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.