  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.
