S&AS: FND: Reliable Semi-Autonomy with Diminishing Reliance on Humans

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Building reliable autonomous systems that can construct and execute plans to achieve some assigned goals, without human intervention, has been the hallmark of artificial intelligence and robotics since their inception. Reliable autonomy is becoming increasingly important as it enables innovative new applications in areas such as transportation, health, and sustainable living. Despite substantial progress, there are still considerable barriers to the long-term, large scale deployment of fully autonomous systems such as self-driving cars or mobile service robots. These barriers range from technological and economic constraints to ethical and legal issues. This project offers a comprehensive approach to circumvent these barriers by building semi-autonomous systems that rely on rich forms of human assistance, ranging from advice to constant supervision of the system with the possibility of taking over control. The project develops techniques to assure the safety of such systems when human assistance is delayed and to reduce their reliance on human assistance over time. Additionally, the project contributes to training of undergraduate and graduate students in this interdisciplinary area, mentoring of students with special attention to underrepresented groups, outreach activities to local schools, and strengthening of industrial collaborations. The project answers fundamental questions about the feasibility, efficiency, and scalability of planning and learning algorithms to support semi-autonomous systems. The main thrusts of the project are (1) develop techniques that can delegate autonomy to a system with some restrictions, and provide strong guarantees that these restrictions will be respected and that the system will maintain a safe state even when human assistance is delayed; (2) develop planning and learning algorithms that are cognizant of the availability of rich forms of human assistance and can effectively factor such assistive actions into the overall plan; (3) handle the high computational complexity of optimizing the interaction with humans under uncertainty and partial observability by creating a hierarchical multi-objective decision model; and (4) leverage human assistance to enable robust and accurate mapping and navigation in new areas, while reducing the reliance on human supervision over time. The project evaluates these capabilities in complex realistic settings involving a campus-scale robot deployment, a driving simulator, and autonomous vehicles in collaboration with Nissan.