arXiv — NLP / Computation & Language · · 3 min read

How Should I Pick a Foundation Model for My Robot? In Favor of a Community Evaluation Framework for Social Robots

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Computer Science > Robotics

arXiv:2608.06898 (cs)
[Submitted on 7 Aug 2026]

Title:How Should I Pick a Foundation Model for My Robot? In Favor of a Community Evaluation Framework for Social Robots

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Abstract:Researchers who seek to build social robot applications on foundation models are faced with a difficult question: how should we pick a model? Public leaderboards offer little guidance: the demands of real-time, embodied social interaction lie largely outside their focus. And direct evaluation is impractical at scale: each embodied study requires scarce participant, robot, and experimenter time. In this paper, we identify five evaluation dimensions for foundation models in social robots: (i) conversational competence, (ii) user safety, (iii) embodied character, (iv) target scene effectiveness, and (v) audience appropriateness. To make model selection cheaper and better informed, we propose a three-tiered evaluation funnel paradigm that first filters with general metrics, then extends to simulated interactions, and terminates in more expensive, robot-specific evaluation. We map all five dimensions across all three tiers, chart where applicable evaluation methods exist and are missing, and close with a call to action: let's build the evaluation framework together as a community.
Comments: 5 pages, 1 figure, 1 table. Accepted at the FoRMA workshop (Foundation Models in the RO-MAN Age: Responsible Development for Social Robotics) at IEEE RO-MAN 2026, Kitakyushu, Japan. Workshop homepage: this https URL
Subjects: Robotics (cs.RO); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
ACM classes: I.2.9; I.2.7; H.5.2
Cite as: arXiv:2608.06898 [cs.RO]
  (or arXiv:2608.06898v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2608.06898
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Eric Nichols [view email]
[v1] Fri, 7 Aug 2026 07:33:25 UTC (27 KB)
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