CRII: CHS: Leveraging Implicit Human Cues to Design Effective Behaviors for Collaborative Robots

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

Robots have the potential to significantly benefit society by actively collaborating with people in critical domains including manufacturing, healthcare, and space exploration. But to provide effective assistance, robots must be able to work with people in a natural, intuitive, and socially adept manner. Current human-robot collaborations require that people explicitly communicate their goals and desired responses to robotic partners. As a result, joint human-robot activities bear little resemblance to scenarios involving human-human teamwork, where people are able to understand their partner's implicit cues, such as eye gaze, facial expressions, and intonations, and intuit appropriate responses, such as moving to a certain location, preemptively fetching a tool, or providing a clarification. The PI's goal in this project is to establish a research program that will explore the design of effective behaviors for collaborative robots by developing computational models that enable them to sense implicit human communicative cues and guide robot responses by inferring cue intent, and to evaluate the effectiveness of the new algorithms in human-robot studies. The research holds significant promise of benefiting society by helping to achieve a vision of robots acting as key contributors, partners, and assistants in human work, with applications across a range of activities including domestic housework, manufacturing, construction, healthcare, and space exploration. In addition to disseminating project outcomes to the larger research community, the PI will build on his successful past outreach activities to provide opportunities for K-12 summer programs centered on robotics and computer science education. To these ends, the PI will address the challenge of designing effective collaborative robots by developing a preliminary framework, process, and set of methods to sense and respond to implicit human communicative behaviors. His approach will involve (1) observing and classifying implicit cues and responses for human-human teams engaged in an archetypical collaborative task, (2) developing computational models of the relationships between goals, cues, and responses using features and parameters extracted from observed behaviors, (3) integrating implicit cue sensing and response algorithms to guide robot behaviors in specific collaborative use cases, and (4) evaluating the effectiveness of these behaviors on collaborative task outcomes. This research will produce a set of generalizable design principles for collaborative robots, generate open-source algorithms showcasing practical implementations, and advance knowledge regarding computational understanding of human behaviors. Overall, the work will lead to robots that are able to work more effectively with people and accelerate the integration of assistive robots into society. It will synthesize theories of human communication and explore their application to human-robot interaction, as well as advancing knowledge regarding how robots might provide assistance as human collaborators and the types of sensors necessary for robots working closely with human partners. Implicit sensing and response algorithms that have been empirically validated in HRI experiments will be disseminated as modules for the open-source Robotic Operating System (ROS).