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In prior work, we proposed an autonomous object pick-and-sort procedure for an industrial robotics application called Gilbreth. In this work, we developed improvements to two critical components of this application: object recognition and motion planning, integrated the new modules to create Gilbreth 2.0, and evaluated its performance. We used a Convolutional Neural Network (CNN) based object-recognition technique, which reduced object recognition time by a factor of 10 when compared to our previous solution, which used correspondence grouping. But this reduction in object recognition time came at a cost of requiring CNN model training time, which was 3 hours with just 13 object types. Our motion planning pipeline improvement was primarily to place constraints on the time threshold for each phase of the robot arm motion. This change enabled an improvement in the percentage of successful trajectories …