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Charting the Growth of Social-Physical HRI (spHRI): A Sys...
[Submitted on 24 Jun 2026] · 2026-06-26 · via cs updates on arXiv.org

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Abstract:Social-physical human-robot interaction (spHRI) has grown rapidly across robotics, human-computer interaction, human-robot interaction, and haptics. Yet, fragmented terminology and inconsistent methodologies make systematic synthesis difficult. To support scalable review practices, we evaluated the extent to which small language models (SLMs; < 1.5B parameters) can assist with title and abstract screening for a large spHRI systematic review. While no SLMs matched human reviewers' performance, the models operated locally and screened papers orders of magnitude faster. The combined SLM ensemble identified 39 papers reviewers missed, representing 10.29% of the final relevant dataset. These results demonstrate that SLMs can augment, rather than replace, expert reviewers and make large-scale literature reviews accessible and sustainable.

Submission history

From: Alexis E. Block [view email]
[v1] Wed, 24 Jun 2026 21:09:20 UTC (1,757 KB)