Before revision:  Considering the expansion of robot applications in more complex and dynamic environments,it is evident that it is impossible to pre-program all possible object categories . Robots will have to be able to learn new object categories in the field.The network architecture proposed in this work expands from the OrthographicNet, an approach recently proposed by Kasaei et al. , using a deep transfer learning strategy which not only meets the aforementioned requirements but additionally generates a scale and rotation-invariant reference frame for the classification of objects. In its current iteration, the OrthographicNet only uses shape-information. With the addition of multiple color spaces, the upgraded network architecture proposed here, can achieve an even higher descriptiveness while simultaneously increasing the robustness of predictions for similarly shaped objects .
After revision:  Considering the expansion of robot applications in more complex and dynamic environments,it is evident that it is impossible to pre-program all possible object categories . Robots will have to be able to learn new object categories in an open-ended fashion while working in the environment.Towards this goal, we propose a deep transfer learning approach to generate a scale- and pose-invariant object representation by considering shape and texture information in multiple colorspaces. The obtained global object representation is then fed to an instance-based object category learning and recognition,where a non-expert human user exists in the learning loop and can interactively guide the process of experience acquisition by teaching new object categories, or by correcting insufficient or erroneous categories. In this work, shape information encodes the common patterns of all categories, while texture information is used to describes the appearance of each instance in detail .