Leah Perlmutter

Papers

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About

Leah Perlmutter is a researcher whose work sits at the intersection of human-robot interaction, situated language understanding, and transparent communication. Her primary contributions focus on bridging the communication gap between humans and robots, particularly in collaborative environments. Perlmutter’s most cited work, “Situated Language Understanding with Human-like and Visualization-Based Transparency” (2016), with 25 citations, tackles the challenge of robots’ “opacity” by introducing transparency mechanisms that help users form accurate mental models of a robot’s perception and reasoning. This foundational paper explores how human-like cues and visualizations can make robot decision-making more legible. Her second most cited paper, “Robot Object Referencing through Legible Situated Projections” (2019, 14 citations), advances this theme by using projected visual information to enable robots to clearly reference objects during task-oriented collaborations. This work is notable for its practical application of augmented reality-like projections as a communication channel. Perlmutter has also explored adaptive robot tutoring (2016) and context-aware video compression for mobile robots (2011), demonstrating a broad interest in making robotic systems more intuitive and efficient. Her research is particularly impactful for students and researchers in human-robot interaction, as it directly addresses the core challenge of designing robots that can communicate their internal states and intentions clearly, fostering more natural and effective human-robot teamwork.