Pierre Gander

Papers

Total Citations

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About

Pierre Gander is a leading researcher in human-robot interaction (HRI), with a focus on deep learning, user engagement, and social robotics. His work centers on developing computational mechanisms that enable robots to achieve lifelong learning and natural, efficient interactions with humans. Gander’s most-cited paper, "Deep Learning Approaches for User Engagement Detection in Human-Robot Interaction: A Scoping Review" (2025, 7 citations), provides a comprehensive synthesis of AI methods for detecting user engagement, a critical step toward responsive robotic systems. His foundational study, "Vicarious Value Learning and Inference in Human-Human and Human-Robot Interaction" (2019, 7 citations), introduces innovative computational models for a humanoid robotic agent to learn from observing human interactions, addressing a key challenge in lifelong learning. More recently, "Exploring task and social engagement in companion social robots: a comparative analysis of feedback types" (2025, 2 citations) examines how different feedback modalities influence both task performance and social bonding. Gander’s contributions bridge cognitive science and AI, advancing the design of companion robots that are not only functional but socially attuned. His work is highly relevant for students and researchers seeking to understand the intersection of deep learning, social cognition, and autonomous robotics.