Gideon Kowadlo

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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)
This paper describes current progress of a project that uses naïve physics to enable a robot to perform efficient odour localisation. Odour localisation is the problem of finding the source of an odour or other volatile chemical. Performing this effectively could lead to many humanitarian and other valuable applications. Current techniques utilise reactive(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)
To date, robotics has had limited success at operating in unstructured environments. Part of the problem is the lack of commonsense reasoning. One area of commonsense reasoning is Naive Physics, the practice of using intuitive rules to reason about the physical environment. Researchers have explored relevant philosophical issues, attempted to develop(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)
— 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)
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)