Dong Hae Mangalindan

Papers

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About

Dong Hae Mangalindan is a researcher advancing the field of human-robot interaction, with a primary focus on trust dynamics, assistance-seeking, and human-supervised autonomy. Their work explores how autonomous systems—particularly mobile manipulators—can intelligently decide when to seek help from human supervisors to maintain trust and performance. In their most-cited paper, "On Trust-aware Assistance-seeking in Human-Supervised Autonomy" (2023, 9 citations), Mangalindan developed policies for robots to gauge when to request human intervention during object collection tasks, directly addressing the critical challenge of trust erosion. Their 2024 study (7 citations) introduced a dual-task paradigm, revealing how secondary task engagement affects human trust and supervisory attention. Most recently, their 2025 paper (2 citations) investigates trust repair strategies in robot-assisted delivery, modeling how robots can rebuild human confidence after errors. By bridging computational modeling with empirical human studies, Mangalindan’s research provides foundational insights for designing robots that are not only efficient but also socially intelligent—capable of preserving the delicate trust essential for effective human-autonomy teams.