Description 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.