Exploration and Learning for Cognitive Robots

Abstract

Before a future with household robots is really feasible, those robots need to be easily adaptable to novel environments and users, be able to apply previously acquired knowledge, and able to learn from perceiving and interacting with the world and users around them. This thesis proposes a cognitive architecture and a set of underlining methods for automatic knowledge acquisition that will bring household robots one step closer to reality. The thesis was inspired by the cognitive development of babies and subsequently treats the topics: Knowledge representation, Sensory motor integration, Knowledge acquisition & learning and Real world applications. We derived ways to describe and efficiently combine visual information, despite cluttered environments and changing illumination. We propose a method to combine different key-point extractors and to select only a small subset based on their information entropy. Since object appearances can vary in colour, texture and 2D/3D shape, we extract feature vectors from multiple viewpoints of the object and calculate the dominant features, which leads to a more robust object recognition process. This also automatically calculates the most efficient way to store the representative features of unknown objects. This method is similar to the information storage process in humans; an important characteristic of human learning is the pruning of information in the working memory to the most relevant information to be stored in long term memory. In order to detect and segment objects without any prior knowledge on their appearance or their background, we designed a visual-attention module that is able to find the salient parts of an image and clusters them into objects ranked by their saliency. Once these objects are localized, the system must act upon them to efficiently learn their properties. This is achieved by simultaneous exploration and manipulation of the selected - most salient - unknown object. However, such a cognitive system cannot be realized without a robot body, and therefore we designed a simple and affordable robot. Another cognitive ability that a future household robot certainly must have is knowledge accumulation from previous experiences. Consequently, we designed a cognitive architecture that allows a robot that has no pre- insight in the world and its objects to incrementally acquire all its expertise from interaction with the environment. We proposed models of working and long term memory as well as algorithms for novelty detection. Finally, we treat two realized applications for external parties, based on our algorithms: Bin-picking by an industrial robot and action detection in ceiling mounted cameras in elderly homes.