Measuring the Perceived Social Intelligence of Robots

Robotic social intelligence is increasingly important. However, measures of human social intelligence omit basic skills, and robot-specific scales do not focus on social intelligence. We combined human robot interaction concepts of beliefs, desires, and intentions with psychology concepts of behaviors, cognitions, and emotions to create 20 Perceived Social Intelligence (PSI) Scales to comprehensively measure perceptions of robots with a wide range of embodiments and behaviors. Participants rated humanoid and non-humanoid robots interacting with people in five videos. Each scale had one factor and high internal consistency, indicating each measures a coherent construct. Scales capturing perceived social information processing skills (appearing to recognize, adapt to, and predict behaviors, cognitions, and emotions) and scales capturing perceived skills for identifying people (appearing to identify humans, individuals, and groups) correlated strongly with social competence and constituted the Mind and Behavior factors. Social presentation scales (appearing friendly, caring, helpful, trustworthy, and not rude, conceited, or hostile) relate more to Social Response to Robots Scales and Godspeed Indices, form a separate factor, and predict positive feelings about robots and wanting social interaction with them. For a comprehensive measure, researchers can use all PSI 20 scales for free. Alternatively, they can select the most relevant scales for their projects

Multi-Context Socially-Aware Navigation Using Non-Linear Optimization

This presents a framework for a novel Unified Socially-Aware Navigation (USAN) architecture and motivates it for Socially Assistive Robotics (SAR) applications. This approach emphasizes interpersonal distance and how spatial communication can be used to build a unified planner for a human-robot collaborative environment. Socially-Aware Navigation (SAN) is vital for helping humans to feel comfortable and safe around robots; HRI studies have shown the importance of SAN transcends safety and comfort. SAN plays a crucial role in the perceived intelligence, sociability, and social capacity of the robot, thereby increasing the acceptance of the robots in public places. Human environments are very dynamic and pose serious social challenges to robots intended for interactions with people. For robots to cope with the changing dynamics of a situation, there is a need to detect changes in the interaction context. We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Our approach augments the navigation stack of Robot Operating System (ROS) utilizing machine learning and optimization tools. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially-appropriate trajectories for its environment. Our method was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot was able to select and account for social objectives autonomously.We also developed new scales for observing HRI that can measure the perceived social intelligence (PSI) of robots. We validated our PSI scale by evaluating our PaCcET-based local planner; a bystander experiment showed that people perceived robots with socially appropriate navigation strategies as more socially intelligent when compared to robots using traditional navigation strategies

Validation of a PaCcET Socially-Aware Navigation Planner

We present a socially aware navigation algorithm configured on a differential drive robot to validate simulation results. The robot will learn the social norms of four different scenarios, calculate a social goal trajectory, then follow the optimal path. A comparison between the normal navigation stack is presented as well as data comparing the time and position different between the experimentation and simulation results. Overall the robot performed efficiently in all four tasks and followed the expected behaviors such as respecting personal space