  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.
