Sorting objects from a conveyor belt using active perception with a POMDP model

We consider an application where a robot must sort objects traveling on a conveyor belt into different classes. The detector and classifier work on 3D point clouds, but are of course not fully accurate, so they sometimes misclassify objects. We describe this task using a novel model in the formalism of partially observable Markov decision processes. With the objective of finding the correct classes with a small number of observations, we then apply a state-of-the-art POMDP solver to plan a sequence of observations from different viewpoints, as well as the moments when the robot decides the class of the current object (which automatically triggers sorting and moving the conveyor belt). In a first version, observations are carried out only for the object at the end of the conveyor belt, after which we extend the framework to observe multiple objects. The performance with both versions is analyzed in simulations, in which we study the ratio of correct to incorrect classifications and the total number of steps to sort a batch of objects.