Abstract: A computer-implemented system for fully automating the training, auto-tuning, and deployment of machine learning models at customer sites, especially models for robotic grasping tasks. The system automatically: (1) detects/predicts performance degradation of one or more machine learning models; (2) triggers a new model training/fine-tuning job in response to such detection/prediction; and (3) deploys the new model upon training completion, without stopping or pausing the production line.

Abstract: A computer system automatically selects robot end-effectors for pick-and-place applications using a model-predictive control algorithm. The system may select an end-effector to replace an existing end-effector in order to optimize (or at least increase) throughput. The system uses a predictive model of reward, where reward of each potential grasp for each end tool is parameterized by a deep neural network. The system may also use a variety of metrics to evaluate the performance of the tool-selection algorithm, and thereby improve performance of the system.