Evaluating Social Reasoning in Embodied Vision-Language Models

Abstract:Foundation models now serve as reasoning layers for robot teams, but it remains unclear whether embodiment-specialized vision-language models (VLMs) preserve the social-reasoning capabilities of general-purpose VLMs. We evaluate Gemini Robotics-ER on SHREC, a benchmark of real human-robot social interactions, and compare it with published general-purpose VLM baselines. Robotics-ER outperforms GPT-4o-mini on error/competence detection but performs worse on detailed social-attribute judgments; adding a visual frame helps one task while hurting the other. These results suggest embodiment-specialized models may perform differently on social judgments that can be read more directly from observable interaction cues versus those requiring more inference about a person's internal state. Because the comparison spans model families, these differences cannot be attributed to embodiment specialization alone. This matters for multi-robot systems, where a shared foundation model could cause systematic social-perception errors to influence multiple coordination decisions. This work presents an initial evaluation, with broader cross-context and video-based analyses left for future work.

Robotics (cs.RO); Human-Computer Interaction (cs.HC)

arXiv:2610.27496 [cs.RO]

(or arXiv:2610.27496v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.27496 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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