Author

Stephanie M. Lukin

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Conference Aug 2026

Ask-to-Act: Learning when Robots Should Clarify Ambiguous Grounded Instructions

Robots that follow natural-language instructions must decide not only what action to execute, but also whether the current evidence is sufficient to act at all. We present Askto-Act, a framework for grounded robot instruction following that treats clarification as a cost-sensitive decision under uncertainty. The system...

Ekele Ogbadu, Stephanie M. Lukin, Cynthia Matuszek · IEEE International Symposium... · 0 citations

Jul 2026

Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings

A VR-based Human Robot Interaction framework for studying how VLM-assisted robots can support situational awareness in simulated hazardous environments suggests that combining VLM-based robotic perception with immersive visualization is a promising approach for supporting situational awareness in hazardous settings.

Mohammad Eskandari, M. Indukuri, Stephanie M. Lukin et al. · arXiv.org · 0 citations

Jul 2026

Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies

This work evaluates several state-of-the-art VLMs across two datasets and multiple prompting strategies to test whether an explicit distinction between hazard and anomaly changes model behavior, and shows that explicitly separating anomaly from hazard provides a more informative evaluation of VLM safety reasoning and e...

M. Indukuri, Mohammad Eskandari, Sree Nitya Kollu et al. · arXiv.org · 0 citations