HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Real-time human perception is crucial for effective human-robot interaction
(HRI). Large vision-language models (VLMs) offer promising generalizable
perceptual capabilities but often suffer from high latency, which negatively
impacts user experience and limits VLM applicability in real-world scenarios.
To systematically study VLM capabilities in human perception for HRI and
performance-latency trade-offs, we introduce HRIBench, a visual
question-answering (VQA) benchmark designed to evaluate VLMs across a diverse
set of human perceptual tasks critical for HRI. HRIBench covers five key
domains: (1) non-verbal cue understanding, (2) verbal instruction
understanding, (3) human-robot object relationship understanding, (4) social
navigation, and (5) person identification. To construct HRIBench, we collected
data from real-world HRI environments to curate questions for non-verbal cue
understanding, and leveraged publicly available datasets for the remaining four
domains. We curated 200 VQA questions for each domain, resulting in a total of
1000 questions for HRIBench. We then conducted a comprehensive evaluation of
both state-of-the-art closed-source and open-source VLMs (N=11) on HRIBench.
Our results show that, despite their generalizability, current VLMs still
struggle with core perceptual capabilities essential for HRI. Moreover, none of
the models within our experiments demonstrated a satisfactory
performance-latency trade-off suitable for real-time deployment, underscoring
the need for future research on developing smaller, low-latency VLMs with
improved human perception capabilities. HRIBench and our results can be found
in this Github repository: https://github.com/interaction-lab/HRIBench.
