NSF Award
2331763
This project aims to develop new technology to ensure the safety of autonomous robotic systems such as self-driving cars and home robots. Safety for such systems is critical because robots interact with the physical world and people, resulting in potential for adverse interaction outcomes in the absence of safety. Most next generation robotic systems are expected to contain components built using machine learning. Such learning-based components can result in new capabilities but can also be prone to unpredictable behavior or failures, making the full system unsafe to use. This project seeks to develop a general solution to this problem. The main idea is to build a safety supervisor, a software module that continuously monitors the actions of a robot and intervenes as needed to ensure safety. The safety supervisor functions similarly to a driving coach, who watches the practicing driver and takes over when necessary. Techniques developed in this project will be broadly useful for building safe and effective robotic systems. Research in this project is integrated with K12, undergraduate, and graduate education through research training, course development and outreach events.<br/><br/><br/>This project develops techniques for constructing a vision-based safety supervisor that endows the full system with the safety property called graceful degradation, meaning that the full system will not fail catastrophically under unfamiliar or unknown scenarios; instead, the full system will detect the unfamiliar nature of the circumstance and switch to actions that are safe and conservative. To this end, the project team develops symbolic scene representations together with reasoning algorithms which produce interpretable and verifiable safety assessments and decisions that are robust to unfamiliar scenarios. To rigorously test and evaluate the safety supervisor, the project team develops algorithms for procedural validation: validation through procedurally generated synthetic visual data. Procedural generation is the process of generating synthetic data from symbolic computer programs, which provide full control at all levels of granularity and easily enable systematic simulation of long-tail events and novel scenarios. In addition to procedural validation, the project team also performs evaluation on real-world robots with a focus on navigation and rearrangement tasks.<br/><br/>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.
