Online memory learning for active object recognition Traditional CNNbased recognition algorithms are trained for limited labeled data, which may not perform well in a different environment due to the lack of adaptivity of the CNN networks. So the traditional CNNbased recognition algorithms can not play a good role in robot applications because the robots have to work in different environments. However, the robot can continuously perceive new images during its mission. These images contain lots of environmentrelated features but lack of labels. So the robots must learn the environmentrelated features adaptively with unlabeled data to further improve the performance of CNNbased recognition algorithms. We call this ability as active object recognition (OBR). In this paper, we designed a dynamic memory structure (DMS) which can adaptively learn the environmentrelated features online and embedded DMS into a VGG16 network to implement active object recognition. We also evaluate our dynamic memory network of CIFAR10 and CIFAR100 classification dataset. The results show that by learning environmentrelated features, dynamic memory network achieves a better performance on classification accuracy. More importantly, the network can have the ability to improve itself while many times testing.

Towards a Robot Architecture for Situated Lifelong Object Learning The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.