Annual Report 2002 - Ãrebro universitet

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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.
58 AASS – Center for Applied Autonomous Sensor Systems 2.4.2 Research Projects Project: Hierarchical Learning of Robot Behaviours Project leader: Tom Duckett Project staff: Li Jun Funding: KK and Faculty funding. Cooperation: not yet Synopsis: This project will develop a generic architecture for learning sensor-motor behaviours in robotic systems. The robot will develop its own internal representations through a process of incremental on-line learning. One application of this technology would be programming by demonstration, where the human operator simply shows the robot what to do (e.g., ”water my plants”, ”clean my bathroom”, etc.). The behaviours acquired will be complex, non-linear and non-achievable by a purely reactive system, so the learning system must incorporate representations that encode the temporal and spatial context of the task and environment. A particular challenge will be to carry out all of the learning in real-time. The project investigates the implementation of a hierarchy of fast, on-line learning algorithms that are integrated by layered learning. The techniques investigated include, but are not limited to, embedded machine learning algorithms such as self-organisation, recurrent artificial neural networks and reinforcement learning. Results in 2002: - Analysis and design of a neural network architecture for behaviour learning incorporating unsupervised and supervised learning. An experimental analysis has been conducted for the application of mobile robot control. - Paper published in Swedish Workshop on Autonomous Robots, 2002. Future developments: - The next experiments will investigate alternative applications and platforms, e.g., feedback control of a mobile manipulator, and alternative sensory inputs, e.g., vision. - Adaptation of the acquired behaviours through on-line learning - Licentiate thesis: expected 2003 (Li Jun) - PhD-thesis: expected 2005 (Li Jun)
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