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Robot Vision and Autonomous Systems

Machines that learn to visually perceive their environment and to interact with it.

The DIPLECS project aims to design an Artificial Cognitive System capable of learning and adapting to respond in the everyday situations humans take for granted. The primary demonstration of its capability will be providing assistance and advice to the driver of a car. The system will learn by watching humans, how they act and react while driving, building models of their behaviour and predicting what a driver would do when presented with a specific driving scenario.
In the COSPAL architecture we combine techniques from the field of artificial intelligence (AI) for symbolic reasoning and learning of artificial neural networks (ANN) for association of percepts and states in a bidirectional way. We establish feedback loops through the continuous and the symbolic parts of the system, which allow perception-action feedback at several levels in the system. After an initial bootstrapping phase, incremental learning techniques are used to train the system simultaneously at different levels, allowing adaptation and exploration. We expect the COSPAL architecture to allow the design of systems that show to a large extent autonomous behavior.
Adaptive Regression Tracking
This project is concerned with developing fast regression based tracking algorithms, that adapt to target appearance changes online, and remove the need for hard-coded models and an offline learning stage.
Embodied Vehicle Navigation
This project is concerned with the development of embodied cognitive architectures that are applicable to the task of autonomous vehicle navigation.
Color-Based Probabilistic Approach for Point Sets Registration
This project is concerned with the development of a registration framework using a probabilistic approach with low-level features, such as color.

Last updated: 2022-01-20