Stochastic Similarity for Validating Human Control Strategy Models

Abstract

Modeling dynamic human control strategy (HCS), or human skill in response to real-time sensing is becoming an increasingly popular paradigm in many different research areas, such as intelligent vehicle systems, virtual reality, and space robotics. Such models are often learned from experimental data, and as such can be characterized despite the lack of a good physical model. Unfortunately, learned models presently offer few, if any, guarantees in terms of model fidelity to the training data. This is especially true for dynamic reaction skills, where errors can feed back on themselves to generate state and command trajectories uncharacteristic of the source process. Thus, we propose a stochastic similarity measure - based on Hidden Markov Model analysis - capable of comparing and contrasting stochastic, dynamic, multi-dimensional trajectories. This similarity measure is the first step in validating a learned model's fidelity to its training data by comparing the model's dynamic trajectories in the feedback loop to the human's dynamic trajectories. In this paper, we first derive and demonstrate properties of the similarity measure for stochastic systems. We then apply the similarity measure to real-time human driving data by comparing different control strategies among different individuals. We show that the proposed similarity measure outperforms the more traditional Bayes classifier in correctly grouping driving data from the same individual. Finally, we illustrate how the similarity measure can be used in the validation of models which are learned from experimental data, and how we can connect model validation and model learning to iteratively improve our models of human control strategy.