Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models

2026-06-24

AI Summary

This study addresses the inefficiency of systematic literature reviews in social-physical human–robot interaction (spHRI), a field hindered by fragmented terminology and inconsistent methodologies. To overcome this challenge, the work proposes the first integration of locally deployed small language models (SLMs; <1.5B parameters) into the review pipeline, establishing an efficient and sustainable automated screening system that assists domain experts in rapidly evaluating paper titles and abstracts. Operating entirely in a local environment, the approach accelerates the screening process by several orders of magnitude compared to manual methods and uncovers 39 additional relevant publications—constituting 10.29% of the final dataset—that were initially missed by human reviewers. This significantly enhances both the comprehensiveness and reliability of the resulting literature synthesis.

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.

Problem

• social-physical human-robot interaction
• systematic review
• fragmented terminology
• inconsistent methodologies
• literature synthesis

Innovation

• small language models
• systematic literature review
• social-physical human-robot interaction
• title and abstract screening
• SLM ensemble