HMR-1: Hierarchical Massage Robot with Vision-Language-Model for Embodied Healthcare

TL;DR

This paper introduces a hierarchical embodied massage framework utilizing vision-language models, along with a large multimodal dataset, to advance healthcare robotics and establish evaluation benchmarks.

Contribution

It presents a novel hierarchical massage framework with a new multimodal dataset and benchmarks for embodied healthcare tasks, enhancing robustness and standardization.

Findings

The dataset contains over 12,000 images and 174,000 QA pairs.

Fine-tuning Qwen-VL improves acupoint grounding accuracy.

Physical experiments validate the framework's practical effectiveness.

Abstract

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.