Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs

Abstract

Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classification problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efficient approach to learn the classifier and generalize it with a minimal amount of data. A high-fidelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classifier, and for each branch of the decision tree, we reason about the confidence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain confidence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees. I. INTRODUCTIONMany robotics applications require planning and decision making based on what the robot observes in order to complete a task. In this article we study the problem of robotic truck unloading, where the task is to empty the truck by unloading all the boxes. Fig. 1 shows an industrial truck unloading robot and cardboard boxes inside the truck that needs to be unloaded. The robot has two end effectors, one is fixed to the base of the robot and can sweep boxes from the floor, and another suspended with an arm and can pick boxes using a plunger mechanism. The task of emptying the truck can be broken down into small sub-tasks like picking boxes from a certain location or sweeping boxes from the floor. This problem involves both task planning and motion planning. We exploit the fact that our problem allows us to perform task planning and motion planning independently, and hence in this paper, we focus only on the task planning.