CAREER: Democratizing Robot Learning for Assistive Robotics in MCI

The US population is aging, and 18 percent of adults over 60-years of age have Mild Cognitive Impairment (MCI). Of those with MCI, up to 15 percent develop dementia down the line. Unfortunately, there is an increased shortage of healthcare workers, and the cost of at-home nursing care is prohibitive.

To meet this challenge, this Faculty Early Career Development (CAREER) project aims to democratize interactive robot learning to enable care partners (e.g., spouses, children, nurses) to program and personalize a robot's behavior through intuitive modes of interaction to assist the care member (i.e., person with MCI) with activities of daily living. Robot learning is achieved using Learning from Demonstration (LfD), which is about computational methods and interfaces to enable end-users to teach robots new skills through interaction (e.g., skill demonstration).

Traditional robotics deployments typically rely on a large number of experts and create robots that are expensive and not easily adapted. Instead, LfD offers a scalable alternative by leveraging efficient algorithms and interactions with non-expert end users. Despite its potential, and decades of research, LfD has not been deployed broadly, in part because these systems do not provide the care partner end-users with insights into the robot's understanding of the world or how the users can be better teachers.

This award seeks to overcome the limitations of traditional robotics (such as cost and scale) and modern robot learning approaches (the fact that they are relegated to the laboratory) by enabling robot learning to be accessible and scalable to support aging in place for persons with MCI. The eventual goal is to have a robot that can be used in the home of someone with MCI to help them with their daily tasks, and that the robot can be easily programmed by someone who does not have specific technical training.

To achieve these goals, the research team will develop new LfD algorithms and interfaces in partnership with collaborators at the Cognitive Empowerment Program at Emory University, members of the MCI community, clinicians, and researchers at the Georgia Institute of Technology in a transdisciplinary research effort. With the oversight and input from MCI focus groups, the research team will collaboratively execute three research thrusts.

First, the team will conduct transdisciplinary research to collect and open-source a first-of-its-kind, multimodal, longitudinal dataset from care partners interacting with a robot with the goal of programming it via LfD to carry out assistive tasks for care members. Second, the team will formulate novel, explainable Artificial Intelligence techniques enabling users to gain a “theory of mind” of the robot, specifically to foster users gaining insight into the behavior of the robot while learning to collaborate via mixed-initiative LfD interactions.

Third, the team will develop novel, peer-teaching LfD algorithms that enables the robot learner to develop a theory of mind about an LfD teacher (i.e. the care partner alongside the person with MCI), leverage that insight to tutor a human teacher, and provide explicit feedback for how the teacher can provide better instruction to the robot. The success of the techniques developed will be based upon improving the robot’s performance at specific tasks, reducing the amount of time required for the user to train the robot, and improving user experience in terms of workload, stress, and perceived usability of the robot.

The outcome of this research will be open-source datasets, interfaces, and roadmaps guiding researchers in democratizing robots, transitioning LfD from the laboratory to the real world. Finally, the research team will also develop a new educational outreach program in partnership with high school educators to integrate robotics into their classrooms in underserved communities in Georgia.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.