中风是全球死亡和残疾的主要原因之一, 每年影响 1500 万人，并导致 500 万人长期受损。 物理治疗师, 也日益短缺，这使得患者更难获得一致的, 高质量护理。

Stroke is one of the leading causes of death and disability worldwide, affecting 15 million people each year and leaving 5 million with long-term impairments. There的 also a growing shortage of physical therapists, making it harder for patients to access consistent, high-quality care. 

麻省理工学院机械工程师开发的一种新方法将尖端人工智能与真正的人类护理实践相结合。

A new approach developed by MIT mechanical engineers combines cutting edge artificial intelligence with real human care practices. 

“我们的目标是教机器人如何协助物理和职业治疗,不是取代治疗师,而是扩大他们的覆盖范围,”解释说，Johannes Lachner,是麻省理工学院机械工程系(MechE)以及脑与认知科学系的麻省理工-诺和诺德人工智能博士后研究员，完成了这项工作。 “A 我们系统的核心创新是物理治疗师可以使用 AI, 训练自己的机器人，从而实现针对每位患者 需求的个性化, 可扩展支持。”

“Our goal is to teach robots how to assist with physical and occupational therapy, not to replace therapists, but to extend their reach,” explains Johannes Lachner, who completed this work as a MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellow in the Departments of Mechanical Engineering (MechE) and Brain and Cognitive Sciences at MIT. “A core innovation of our system is that physical therapists can train their own robot using AI, enabling personalized, scalable support tailored to each patient的 needs.”

Lachner, 现在是普渡大学, 的助理教授，Noah Geiger, 是 Robert Bosch GmbH 的初级经理计划实习生 (AI/IT Track)，前 MIT, 的访问学生开发了一种机器人治疗系统，该系统向物理治疗师学习，以适应性地支持中风患者。将基于变压器的扩散模型与实时力反馈,相结合，双臂机器人可以根据每个患者’的能力安全地调整帮助。

Lachner, now an assistant professor at Purdue University, and Noah Geiger, a Junior Managers Program Trainee (AI/IT Track) at Robert Bosch GmbH and former visiting student at MIT, developed a robotic therapy system that learns from physical therapists to adaptively support stroke patients. Combining transformer-based diffusion models with real-time force feedback, the dual-arm robot safely adjusts assistance based on each patient的 capabilities.

该系统使用与 ChatGPT 生成图像, 类似的人工智能技术，但, 它学习机器人应该如何表现，以便在物理治疗期间最好地支持患者。然后，机器人根据所学到的知识来帮助患者,，提供适量的身体帮助，以保持患者的挑战和参与度。

The system uses similar AI technology to that of ChatGPT generating images, but instead, it learns how a robot should behave to best support a patient during physical therapy. The robot then assists the patient based on what it has learned, providing just the right amount of physical assistance to keep the patient challenged and engaged.

“虽然机器人技术中的大多数生成人工智能模型都专注于运动,，但我们的模型是最先学习物理交互的模型之一,，即,如何响应触摸,力,和阻力,”盖革说。 “我们的生成式 AI 模型的新颖之处在于它能够学习运动规划之外的动态物理交互。”

“While most generative AI models in robotics focus on motion, ours is among the first to learn physical interaction, i.e., how to respond to touch, force, and resistance,” says Geiger. “The novelty of our generative AI model is its ability to learn dynamic physical interaction beyond motion planning.”

通过对健康参与者, 进行测试的初始原型，研究人员使用真实世界的物理遥控实验训练了生成式 AI 模型。参与者进行了常见的康复运动,，例如手臂举起和平面外到达,，同时有意改变他们的努力程度。

Through an initial prototype tested with healthy participants, the researchers trained a generative AI model using real-world physical telemanipulation experiments. Participants performed common rehabilitation movements, such as arm lifting and out-of-plane reaching, while intentionally varying their level of effort. 

“这使模型能够了解机器人辅助应如何适应患者'的积极参与,” Lachner 解释道。 “同时,人类操作员使用遥控,引导机器人完成接触丰富的操作任务，创建‘物理跑酷运动。’一起,这些互补的数据集使模型能够学习和推广有效的物理交互策略。”

“This enabled the model to learn how robotic assistance should adapt to the patient的 active participation,” Lachner explains. “In parallel, human operators guided the robot through contact-rich manipulation tasks using telemanipulation, creating a ‘physical parkour of motion. Together, these complementary datasets enabled the model to learn and generalize effective physical interaction strategies.”

“初步机器人实验已经证明了这种方法的可行性,” Lachner 报告。 “该项目的下一阶段旨在开发治疗师特定模型，并在长期临床研究中对之前接受手法治疗的相同患者进行评估。”

“Initial robotic experiments have already demonstrated the feasibility of this approach,” Lachner reports. “The next phase of the project aims to develop therapist-specific models and evaluate them in a long-term clinical study with the same patients who previously received manual therapy.”

该系统可以支持广泛的物理治疗需求。主要例子包括上肢损伤的中风患者, 手术后努力恢复活动范围的患者, 以及需要保持手臂和肩膀力量和活动能力的老年人 — 但, Lachner 说, 其他未来应用也是可能的。

The system can support a wide range of physical therapy needs. Key examples include stroke patients with upper limb impairments, post-surgical patients working to regain range of motion, and older adults who need to maintain strength and mobility in their arms and shoulders — but, Lachner says, other future applications are possible. 

“展望未来,我们的方法可能远远超出物理治疗,”他说。 “它有潜力教机器人如何与世界进行物理交互,，例如工业环境或协作工作空间中的,。通过将基于物理的控制与 AI, 相结合，我们 正在实现新一代物理智能,、稳定, 和安全的机器人。”

“Looking ahead, our approach could go far beyond physical therapy,” he says. “It has the potential to teach robots how to physically interact with the world, for example, in industrial settings or collaborative workspaces. By combining physics-grounded control with AI, we’re enabling a new generation of physically intelligent, stable, and safe robots.”  

这项研究是通过麻省理工学院诺和诺德人工智能博士后研究员计划的资助和德国 KUKA Robotics, 的支持而得以实现的。有关该项目的更多信息可以在项目网站上找到。

This research was made possible through funding from the MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellows Program and support from KUKA Robotics, Germany. More information about the project can be found on the project website.