CAREER: Safety Assurances for Perception-Enabled Robotic Systems

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From self-driving vehicles to autonomous drones, machine learning-driven perception components constitute a core part of modern autonomous systems and robots. Autonomous system capabilities are primarily enabled by the ability of modern machine learning methods to elegantly process rich perceptual inputs and outputs so as to produce useful information for control, ultimately enabling robots to make intelligent decisions in novel situations based on what they see. However, perception failures can cascade to catastrophic robot failures and compromise human safety, as exemplified by recent self-driving car accidents. Therefore, ensuring safe robot operation under learning-driven, perception-based controllers is paramount to enable their adoption in high-integrity and safety-critical applications. This project will establish a foundational framework for providing continual safety assurances for closed-loop systems under a perception-based controller, wherein assurances are provided provisionally at training time, and continually monitored, updated, and improved during operation-time (or runtime). In particular, this project will: (a) develop novel techniques for learning robust-by-construction perception policies; (b) construct safety monitors for perception policies to ensure their safe operation during runtime; and (c) develop a principled approach to mine closed-loop perception failures at scale and use them to improve robot safety over time. These results will be grounded through a thorough evaluation on a heterogeneous physical robotic testbed, as well as photorealistic simulators, with a focus on autonomous inspection and autonomous aircraft landing tasks. The ability to develop safe perception-driven systems will have a direct, positive impact on a broad range of robotics applications where safety and reliability are of high importance, such as surveillance of critical infrastructure, service or delivery robots, and autonomous cars. This impact will be enhanced through: (a) an integrated education and outreach plan designed to facilitate robot safety discussions and educate faculty and students at all levels: K-12, undergraduate and graduate students, and the broader robotics research community; (b) close collaborations with industry and regulatory bodies; and (c) focusing on disseminating codebases and implementations, and open-sourcing curriculum materials for a new robotics course including hands-on labs with wheeled robots.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.