Abstract. With the decline of rural populations across the globe, much hope is vested in the use of robots in agriculture as a means to sustain food production. This is particularly relevant for high-value crops, such as strawberries, where harvesting is currently very labour-intensive. As part of a larger project to build a robot that is capable of harvesting strawberries, we have studied the identification of the picking point of strawberries -the point that a robot hand should grasp the strawberry -from images of strawberries. We present a novel approach to identify the picking point and evaluate this approach.

Strawberries are an important cash crop that are grown worldwide. They are also a labour-intensive crop, with harvesting a particularly labour-intensive task because the fruit needs careful handling. This project investigates collaborative human-robot strawberry harvesting, where interacting with a human potentially increases the adaptability of a robot to work in more complex environments. The project mainly concentrates on two aspects of the problem: the identification of the fruit and the picking of the fruit. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computer systems organization → Robotic control; • Computing methodologies → Vision for robotics.

In this paper, a grounding framework is proposed that combines unsupervised and supervised grounding by extending an unsupervised grounding model with a mechanism to learn from explicit human teaching. To investigate whether explicit teaching improves the sample efficiency of the original model, both models are evaluated through an interaction experiment between a human tutor and a robot in which synonymous shape, color, and action words are grounded through geometric object characteristics, color histograms, and kinematic joint features. The results show that explicit teaching improves the sample efficiency of the unsupervised baseline model.

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This paper presents the results of preliminary experiments in humanrobot collaboration for an agricultural task. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computer systems organization → Robotic control; • Computing methodologies → Vision for robotics.