Accurate vision-based action recognition is crucial for developing autonomous robots that can operate safely and reliably in complex, real-world environments. In this work, we advance video-based recognition of indoor daily actions for robotic perception by leveraging vision-language models (VLMs) enriched with domain-specific knowledge. We adapt a prompt-learning framework in which class-level textual descriptions of each action are embedded as learnable prompts into a frozen pre-trained VLM backbone. Several strategies for structuring and encoding these textual descriptions are designed and evaluated. Experiments on the ETRI-Activity3D dataset demonstrate that our method, using only RGB video inputs at test time, achieves over 95\% accuracy and outperforms state-of-the-art approaches. These results highlight the effectiveness of knowledge-augmented prompts in enabling robust action recognition with minimal supervision.
@article{arxiv.2509.16452,
title = {KRAST: Knowledge-Augmented Robotic Action Recognition with Structured Text for Vision-Language Models},
author = {Son Hai Nguyen and Diwei Wang and Jinhyeok Jang and Hyewon Seo},
journal= {arXiv preprint arXiv:2509.16452},
year = {2025}
}