Laura Battistel
Papers
1
Total Citations
2
H-Index
1
About
Laura Battistel investigates the critical intersection of human cognition and robotics, with a primary focus on how people perceive and calibrate trust in autonomous systems. Her work addresses a fundamental challenge in human-robot teaming: the human operator’s ability to accurately assess a robot’s changing reliability over time. In her most-cited paper, “Difficulties in Perceiving and Understanding Robot Reliability Changes in a Sequential Binary Task” (2024), she employs virtual reality (VR) to demonstrate that humans often struggle to detect subtle shifts in robot performance, a finding with profound implications for designing more intuitive human-robot interfaces. This research, already garnering early citations, highlights her contribution to trust calibration theory—a cornerstone of effective collaboration between humans and machines. Battistel’s work is notable for its methodological rigor, combining VR-based experimentation with cognitive psychology frameworks to reveal systematic biases in human perception of robotic agents. Her findings not only advance academic understanding of human-robot interaction but also offer practical guidance for engineers developing adaptive autonomous systems. As a rising voice in this field, Battistel’s research promises to shape how future teams of humans and robots work together safely and efficiently.
Research Focus
Key Achievements
Top Papers
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