Perception & Reasoning
Fuse visual, spatial, and linguistic input to understand physical environments and identify uncertainty.
An interdisciplinary tutorial on enabling embodied agents to perceive, clarify, personalize, and safely collaborate with humans in the shared physical world.
HEMI bridges vision-language representation, human-agent interaction, and social sciences for trustworthy agents in dynamic real-world environments.
Fuse visual, spatial, and linguistic input to understand physical environments and identify uncertainty.
Use verbal and non-verbal cues to clarify ambiguous intent and prevent risks during action.
Adapt agent behavior and communication styles to situated context and individual preferences.
Understand how MLLMs and VLAs ground multimodal input.
Resolve ambiguity through appropriate human-agent dialogue.
Tailor behavior, action, and communication to users.
Bring gaze, posture, and gestures into embodied AI.

Embodied perception, semantic understanding, and navigation/manipulation.

Multimodal learning, vision-language model adaptation, and human-AI interaction.

Computational Social Intelligence and social signal processing.
This site is the central hub for supporting material.
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