The aptitude to identify the emotional states of others and response to exposed emotions is an important aspect of human social intelligence. Robots are expected to be prevalent in society to assist humans in various tasks. Human-robot interaction (HRI) is of critical importance in the assistive robotics sector. Smart digital assistants and assistive robots fail quite often when a request is not well defined verbally. When the assistant fails to provide services as desired, the person may exhibit an emotional response such as anger or frustration through expressions in their face and voice. It is critical that robots understand not only the language, but also human psychology. A novel affection-based perception architecture for cooperative HRIs is studied in this paper, where the agent is expected to recognize human emotional states, thus encourages a natural bonding between the human and the robotic artifact. We propose a method to close the loop using measured emotions to grade HRIs. This metric will be used as a reward mechanism to adjust the assistant's behavior adaptively. Emotion levels from users are detected through vision and speech inputs processed by deep neural networks (NNs). Negative emotions exhibit a change in performance until the user is satisfied.

As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown. Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance. The overlap among clusters is another major challenge for the existing stream clustering methods. These constraints strongly limit their real-time applications. In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed. It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem. It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers. To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm. The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided. Results show comparable or better performance relative to five popular data stream clustering algorithms. A scalability analysis of the proposed streaming clustering method is presented in this paper as well.

Simultaneous Localization and Mapping (SLAM) is one of the most widely popular and applied methods designed for more accurate localization and navigation operations. Our experiments showed that vision based mapping helps agents navigate in unknown environments using feature based mapping and localization. Instead of using a classical monocular camera as a vision source, we have decided to use RGB-D (Red, Green, Blue, Depth) cameras for better feature detection, 3D mapping, and localization. This is due to the fact that the RGB-D camera returns depth data as well as the normal RGB data. Moreover, we have applied this method on multiple robots using the concept of cooperative SLAM. This paper illustrates our current research findings and proposes a new architecture based on gathered data from RGB-D cameras, which are the Microsoft Kinect and the ASUS Xtion Pro for 3D mapping and localization.