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Odour localisation in an enclosed area is difficult due to the formation of sectors of circulating airflow. Well-defined plumes do not exist, and reactive plume following may not be possible. Odour localisation has been partially achieved in this environment by using knowledge of airflow, and a search that relies on chemical sensing and reasoning. However(More)
Previous work on odour localisation in enclosed environments, relying on an airflow model, has faced significant limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are(More)
This paper describes current progress of a project, which uses na¨ıve physics to enable a robot to perform efficient odor localization. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require the robot to follow the odor plume along its entire length, which is time consuming and may be(More)
This paper presents a new intelligent odor localization strategy, which enables a robot to locate the source of an odor in a cluttered indoor environment. Traditionally, work in this area has focused on open areas free of obstacles and having no walls or possessing walls without openings. Existing solutions predominantly use reactive algorithms to navigate(More)
The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have been widely applied to unsupervised learning problems, for both classification and prediction. To date, there has been no attempt to incorporate MPF/HTM in reinforcement learning or other adaptive systems; that is, to use knowledge embodied within the(More)
We present a probabilistic, salience-based mechanism for the interpretation of pointing gestures together with spoken utterances. Our formulation models dependencies between spatial and temporal aspects of gestures and features of objects. The results from our corpus-based evaluation show that the incorporation of pointing information improves(More)
We present a probabilistic, salience-based approach to the interpretation of pointing gestures together with spoken utterances. Our mechanism models dependencies between spatial and temporal aspects of gestures and features of utterances. For our evaluation, we collected a corpus of requests which optionally included pointing. Our results show that pointing(More)