Rachel Schlossman

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About

Rachel Schlossman advances the frontier of human-robot interaction, with a focus on creating autonomous service robots that are both capable and trustworthy. Her work is anchored in two critical challenges: enabling robots to perform complex domestic tasks autonomously, and ensuring their behavior is formally verifiable. As a key contributor to the UT Austin Villa@Home team, she helped develop integrated AI systems for the RoboCup@Home competition, tackling real-world problems in perception, manipulation, and task planning. Her most cited paper, the team’s 2019 competition report (4 citations), details these integrated solutions. More distinctively, Schlossman’s research on synthesizing human-aware robot controllers introduces formal guarantees into human-robot interaction. Her paper on this topic (2 citations) addresses the critical need for robots to not only assist humans but to do so with provable safety and goal satisfaction. By bridging the gap between high-level AI task execution and low-level formal verification, Schlossman is pioneering a path toward robots that are both practically useful and demonstrably reliable—a crucial step for deploying service robots in real human environments.