Cost-Effective Sensing for Goal Inference: A Model Predictive Approach

Abstract

Goal inference is of great importance for a variety of applications that involve interaction, coordination, and/or competition with goal-oriented agents. Typical goal inference approaches use as many pointwise measurements of the agent's trajectory as possible to pursue a most accurate a-posteriori estimate of the goal. However, taking frequent measurements may not be preferred in situations where sensing is associated with high cost (e.g., sensing + perception may involve high computational/bandwidth cost and sensing may raise security concerns in privacy-critical/data-sensitive applications). In such situations, a sensible tradeoff between the information gained from measurements and the cost associated with sensing actions is highly desirable. This paper introduces a cost-effective sensing strategy for goal inference tasks based on hybrid Kalman filtering and model predictive control. Our key insights include: 1) a model predictive approach can be used to predict the amount of information gained from new measurements over a horizon and thus to optimize the tradeoff between information gain and sensing action cost, and 2) the high computational efficiency of hybrid Kalman filtering can ensure real-time feasibility of such a model predictive approach. We evaluate the proposed cost-effective sensing approach in a goal-oriented task, where we show that compared to standard goal inference approaches, our approach takes a considerably reduced number of measurements while not impairing the speed, accuracy, and reliability of goal inference by taking measurements smartly.