Abstract. Robot perception and computer vision have witnessed an unprecedented progress in the last decade. Robots and autonomous vehicles are now able to detect objects, localize them, and create large-scale maps of an unknown environment, which are crucial capabilities for navigation and manipulation. Despite these advances, both researchers and practitioners are well-aware of the brittleness of current perception systems, and a large gap still separates robot and human perception. While many applications can afford occasional failures (e.g., AR/VR, domestic robotics), high-integrity autonomous systems (including self-driving vehicles) demand a new generation of algorithms. This talk discusses two efforts targeted at bridging this gap. The first focuses on robust algorithms: I present recent advances in the design of certifiable perception algorithms that are robust to extreme amounts of outliers and afford performance guarantees. I show applications of our algorithms to object pose and shape estimation and SLAM (Simultaneous Localization and Mapping) and discuss recent work that bridges certification with self-supervised learning for object pose estimation. The second effort targets system-level monitoring. I present recent work on runtime perception-system monitoring and discuss algorithms and performance guarantees for fault detection and identification in complex perception systems.

Bio. Luca Carlone is the Leonardo Career Development Associate Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He received his PhD from the Polytechnic University of Turin in 2012. He joined LIDS as a postdoctoral associate (2015) and later as a Research Scientist (2016), after spending two years as a postdoctoral fellow at the Georgia Institute of Technology (2013-2015). His research interests include nonlinear estimation, numerical and distributed optimization, and probabilistic inference, applied to sensing, perception, and decision-making in single and multi-robot systems. His work includes seminal results on certifiably correct algorithms for localization and mapping, as well as approaches for visual-inertial navigation and distributed mapping. He is a recipient of the Best StudentPaper Award at IROS 2021, the Best Paper Award in Robot Vision at ICRA 2020, a 2020 Honorable Mention from the IEEE Robotics and Automation Letters,aTrack Best Paper award at the 2021 IEEEAerospaceConference, the 2017 Transactions on Robotics King-Sun Fu Memorial Best Paper Award, the Best PaperAward at WAFR2016, the Best Student Paper Award at the 2018 Symposium on VLSI Circuits, and he was best paper finalist at RSS2015and RSS 2021. He is also a recipient of the AIAA Aeronautics and Astronautics Advising Award(2022), the NSF CAREER Award (2021), the RSS Early Career Award (2020), the Google Daydream (2019) and the Amazon Research Award (2020, 2022), and the MIT AeroAstro VickieKerrebrock Faculty Award (2020). He is an IEEE senior member andanAIAAassociate fellow. At MIT, he teaches “Robotics: Science and Systems,” the introduction to robotics for MIT undergraduates, and he created the graduate-level course “Visual Navigation for Autonomous Vehicles”, which covers mathematical foundations and fast C++ implementations of spatial perception algorithms for drones and autonomous vehicles.