Cognitive Systems and Robotics Actions performed at plants interacting with humans The GARNICS project aims at 3D sensing of plant growth and building perceptual representations for learning the links to actions of a robot gardener. Plants are complex, self-changing systems with increasing complexity over time. Actions performed at plants (like watering), will have strongly delayed effects. Thus, monitoring and controlling plants is a difficult perception-action problem requiring advanced predictive cognitive properties, which so far can only be provided by experienced human gardeners. Sensing and control of the actual properties of a plant is relevant to e.g. seed production and plant breeders. Plant models will be acquired and by interacting with a human gardener the system will be taught the different cause-effect relations resulting from possible treatments. The robot gardener will be able to choose from its learned repertoire the appropriate actions for optimal plant growth.
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