Online inference of human belief for cooperative robots

For human-robot cooperation, inferring a hu-man's cognitive state is very important for an efficient and natural interaction. Similar to human-human cooperation, understanding what the partner plans and knowing, if he is situation aware, is necessary to prevent collisions, offer support at the right time, correct mistakes before they happen or choose the best actions for oneself as early as possible. We propose a model-based belief filter to extract relevant aspects of a human's mental state online during cooperation. It performs inference based on human actions and its own task knowledge, modeling cognitive processes like perception and action selection. In contrast to most prior work, we explicitly estimate the human belief instead of inferring only a single mode or intention. Since this is a double inference process, we focus on representing the human estimates of environmental state and task as well as corresponding uncertainties. We designed a human-robot cooperation experiment that allowed for a variety of cognitive states of both agents and collected data to test and evaluate the proposed belief filter. The results are promising, as our system can be used to provide reasonable predictions of the human action and insights into his situation awareness. At the same time it is inferring interpretable information about the underlying cognitive states - A belief about the human's belief about the environment.