Annual Report 2002 - Örebro universitet

Annual Report 2002 57
2.4. Learning systems lab
Our objective is to advance the state-of-the-art in the theory and practice of learning and
adaptation in autonomous sensor systems. This will be achieved by developing strongly
autonomous agents with the capacity to learn from experience and thus to adapt to highly
dynamic and uncertain environments and working scenarios.
These situations are extremely difficult to model by traditional methods of numerical analysis
due to the highly unpredictable nature of both the agent's sensory perceptions and the effects
of motor actions, and the presence of other agents such as humans. We therefore focus on
learning and adaptation through interaction of the agent with the user and environment.
The techniques that we apply include supervised and unsupervised machine learning
algorithms such as artificial neural networks, genetic algorithms, self-organisation, etc.
2.4.1 Focus
The research focus of the Learning Systems Lab is the development of robots and
autonomous systems, which can improve performance by learning from their own sensory
input. At present, the research activities of the lab are organized around two major themes:
1. Learning from machine-human interaction. This includes learning which facilitates
cooperation between humans and machines, e.g., recognition, tracking and identification
of humans by an autonomous robot; knowledge acquisition from a human expert, e.g.,
diagnosis of medical images; and learning of robotic skills from demonstrations by a
human operator.
2. Learning sub-symbolic representations and behaviours for robotic systems. This includes
the acquisition of internal models such as cognitive maps through active exploration of the
environment, and learning of sensor-motor competences that map sensor readings directly
onto motor actions, resulting in complex behaviour grounded in the sensor-motor
experience of the agent.
The research methodology of the Learning Systems Lab is based on developing theoretically
sound solutions to real world problems, and places strong emphasis on a tight coupling
between theory and empirical methods; including the use of performance metrics, quantitative
comparisons of actual systems, and statistical tests of significance.