April 12, 2024
Karen Leung, University of Washington
Autonomous robots are becoming increasingly prevalent in our daily existence, from navigating our roads and sky, assisting in households and warehouses, conducting daring search and rescue missions, and even exploring the frontiers of space. Yet building robots that can safely and fluently interact with humans in a trusted manner remains an elusive task. Ensuring robots can keep a sufficiently safe distance from humans is at odds with fluent interactions, yet humans are remarkable at seamlessly avoiding collision in crowded settings. In this talk, we will study how humans engage in safe and fluent multi-agent interactions and how this can be applied to robot decision-making and control. In the first half of the talk, I will introduce the notion of safety concepts and demonstrate how we can tractably synthesize data-driven safety concepts using control theoretic tools as inductive biases. These data-driven safety concepts are designed to capture how humans think about safety in real-world scenarios more accurately. In the second half, I will present recent work investigating how fluent motion can lead to safer interactions. Specifically, I will show that legible and proactive robot behavior can lead to prosocial interactions. This talk aims to revisit how safety is defined and rethink how safety and fluency can be more compatible with one another in human-robot interactions.
About the speaker: faculty.washington.edu/kymleung/
More about the course can be found here: stanfordasl.github.io/robotic...
View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: • Stanford AA289 - Robot...
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