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The authors introduce MedMassage-12K, a multimodal dataset of 12,190 images with QA pairs, designed to address the lack of standardized benchmarks for embodied healthcare, specifically acupoint massage. They propose a hierarchical embodied massage framework (HMR-1) using a high-level acupoint grounding module (MLLM-based) and a low-level control module for trajectory planning. Experiments, including fine-tuning Qwen-VL and physical robot trials, validate the framework's effectiveness and establish a benchmark for embodied massage tasks.
Forget generic robot demos – this work introduces a complete pipeline and dataset for AI-powered massage robots that can understand language and identify acupoints.
The rapid advancement of Embodied Intelligence has opened transformative opportunities in healthcare, particularly in physical therapy and rehabilitation. However, critical challenges remain in developing robust embodied healthcare solutions, such as the lack of standardized evaluation benchmarks and the scarcity of open-source multimodal acupoint massage datasets. To address these gaps, we construct MedMassage-12K - a multimodal dataset containing 12,190 images with 174,177 QA pairs, covering diverse lighting conditions and backgrounds. Furthermore, we propose a hierarchical embodied massage framework, which includes a high-level acupoint grounding module and a low-level control module. The high-level acupoint grounding module uses multimodal large language models to understand human language and identify acupoint locations, while the low-level control module provides the planned trajectory. Based on this, we evaluate existing MLLMs and establish a benchmark for embodied massage tasks. Additionally, we fine-tune the Qwen-VL model, demonstrating the framework's effectiveness. Physical experiments further confirm the practical applicability of the framework.Our dataset and code are publicly available at https://github.com/Xiaofeng-Han-Res/HMR-1.
