Uncertainty-Aware Self-Supervised Target-Mass Grasping of Granular Foods
This addresses the challenge of rapid adaptation to new foods in the food packing industry, though it is incremental as it builds on existing deep learning and uncertainty estimation methods.
The study tackled the problem of robots learning to grasp specific amounts of diverse granular foods with limited training data, achieving significantly improved grasp accuracy for target masses using smaller datasets by incorporating uncertainty-aware self-supervised learning.
Food packing industry workers typically pick a target amount of food by hand from a food tray and place them in containers. Since menus are diverse and change frequently, robots must adapt and learn to handle new foods in a short time-span. Learning to grasp a specific amount of granular food requires a large training dataset, which is challenging to collect reasonably quickly. In this study, we propose ways to reduce the necessary amount of training data by augmenting a deep neural network with models that estimate its uncertainty through self-supervised learning. To further reduce human effort, we devise a data collection system that automatically generates labels. We build on the idea that we can grasp sufficiently well if there is at least one low-uncertainty (high-confidence) grasp point among the various grasp point candidates. We evaluate the methods we propose in this work on a variety of granular foods -- coffee beans, rice, oatmeal and peanuts -- each of which has a different size, shape and material properties such as volumetric mass density or friction. For these foods, we show significantly improved grasp accuracy of user-specified target masses using smaller datasets by incorporating uncertainty.