Andrea Bajcsy
Assistant Professor, Robotics Institute
Talk Title
Towards Open World Robot Safety
Abstract
Robot safety is a nuanced concept. We commonly equate safety with collision-avoidance, but in complex, real-world environments (i.e., the “open world’’) it can be much more: for example, a mobile manipulator should understand when it is not confident about a requested task, that areas roped off by caution tape should never be breached, and that objects should be gently pulled from clutter to prevent falling. However, designing robots that have such a nuanced safety understanding---and can reliably generate appropriate actions---is an outstanding challenge. In this talk, I will describe my group’s work on systematically uniting modern machine learning models (such as large vision-language models and latent world models) with classical formulations of safety in the control literature to generalize safe robot decision-making to increasingly open world interactions. Throughout the talk, I will present experimental instantiations of these ideas in domains like vision-based navigation and robotic manipulation.
Bio
Andrea Bajcsy is an Assistant Professor in the Robotics Institute at Carnegie Mellon University where she leads the Interactive and Trustworthy Robotics Lab (Intent Lab). She broadly works at the intersection of robotics, machine learning, control theory, and human-AI interaction. Prior to joining CMU, Andrea received her Ph.D. in Electrical Engineering & Computer Science from University of California, Berkeley in 2022. She is the recipient of the NSF CAREER Award (2025), Google Research Scholar Award (2024), Rising Stars in EECS Award (2021), Honorable Mention for the T-RO Best Paper Award (2020), NSF Graduate Research Fellowship (2016), and worked at NVIDIA Research for Autonomous Driving.
Philip Koopman
Associate Professor, Electrical and Computer Engineering
Talk Title
Autonomous Vehicle Safety
Abstract
This talk will give an overview of autonomous vehicle safety, including: getting past the safety rhetoric, safety engineering in a nutshell, why machine learning breaks safety engineering, core ML-related problems for life-critical system safety, the approach of the ANSI/UL 4600 standard for autonomous system safety evaluation, and considerations beyond technical safety metrics.
Bio
Philip Koopman of Carnegie Mellon University is an internationally recognized expert on Autonomous Vehicle (AV) safety whose work in that area spans almost 30 years. He is has also worked extensively in more general embedded system design, software quality, and safety across numerous transportation, industrial, and defense application domains including conventional automotive software and hardware systems. He originated the UL 4600 autonomous vehicle safety standard, and received the Industry Legend award at the 2024 the Self-Driving Industry Awards.