Robots that validate learned perceptual models

Service robots that should operate autonomously need to perform actions reliably, and be able to adapt to their changing environment using learning mechanisms. Optimally, robots should learn continuously but this approach often suffers from problems like over-fitting, drifting or dealing with incomplete data. In this paper, we propose a method to automatically validate autonomously acquired perception models. These perception models are used to localize objects in the environment with the intention of manipulating them with the robot. Our approach verifies the learned perception models by moving the robot, trying to re-detect an object and then to grasp it. From observable failures of these actions and highlevel loop-closures to validate the eventual success, we can derive certain qualities of our models and our environment. We evaluate our approach by using two different detection algorithms, one using 2D RGB data and one using 3D point clouds. We show that our system is able to improve the perception performance significantly by learning which of the models is better in a certain situation and a specific context. We show how additional validation allows for successful continuous learning. The strictest precondition for learning such perceptual models is correct segmentation of objects which is evaluated in a second experiment.