Fruit removal forces of early stage pickling cucumbers for harvest automation

European growers are demanding selective mechanical harvesting solutions, such as robotic solutions, because hand harvesting is extremely labour intensive. Mechanical harvesting depends on low fruit retention forces between fruit and plant. Over two consecutive growing seasons, including a winter greenhouse trial, more than 1 600 samples were taken to determine the fruit removal force of early stage cucumbers for pickling and canning. Compared to studies with other fruit the fruit removal force of cucumbers was fairly high and also showed a large degree of variation. A significant percentage of the harvest had stalk residues, which cause the expenditure of extra man-hours on the processing lines. It was therefore concluded, that mere pulling is not sufficient to disconnect the fruit. Harvesting robots require an extra device for ease of separation

Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting data on hardware can be expensive. This has led to a recent interest in adapting data-efficient learning techniques to robotics. One popular method is Bayesian Optimization (BO), a sample-efficient black-box optimization scheme, but its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge to reduce dimensionality in a meaningful way, with a focus on bipedal locomotion. In previous work, we proposed a transformation based on knowledge of human walking that projected a 16-dimensional controller to a 1-dimensional space. In simulation, this showed enhanced sample efficiency when optimizing human-inspired neuromuscular walking controllers on a humanoid model. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation and on hardware. We present three different walking controllers; two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.Comment: 8 pages, submitted to IEEE International Conference on Robotics and Automation 201