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Analytic modeling, imitation, and experience-based learning are three approaches that enable robots to acquire models of their morphology and skills. In this paper, we combine these three approaches to efficiently gather training data to learn a model of reachability for a typical mobile manipulation task: approaching a worksurface in order to grasp an object. The core of the approach is experience-based...
The notion of affordances that was proposed by J.J. Gibson, refers to the action possibilities offered to the organism by its environment. In a previous formalization, affordances are defined as general relations that pertain to the robot-environment interaction and they are represented as triples which consist of the initial percept of the environment, the behavior applied and the effect produced...
We present a neural network approach to early motor learning. The goal is to explore the needs for boot-strapping the control of hand movements in a biologically plausible learning scenario. The model is applied to the control of hand postures of the humanoid robot ASIMO by means of full upper body movements. For training, we use an efficient online scheme for recurrent reservoir networks consisting...
Robot soccer is an excellent testbed to explore innovative ideas and test the algorithms in multi-agent systems (MAS) research. A soccer team should play in an organized manner in order to score more goals than the opponent, which requires well-developed individual and collaborative skills, such as dribbling the ball, positioning, and passing. However, none of these skills needs to be perfect and...
This paper focuses on developing a team of mobile robots capable of learning via human interaction. A modified Q-learning algorithm incorporating a teacher is proposed. The paper first concentrates on simplifying the Q-learning algorithm to be implemented on small and simple team of robots having limited capabilities of memory and computational power. Second it concentrates on the incorporation of...
The Morris water maze task is a spatial memory task in which an association between cues from the environment and position must be learned in order to locate a hidden platform. This paper details the results of using a temporal difference (TD) learning approach to learn associations between perceptual states, which are discretized using a self organizing map (SOM), and actions necessary for a robot...
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