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’s also a growing shortage of physical therapists, making it harder for patients to access consistent, high-quality care.
A new approach developed by MIT mechanical engineers combines cutting edge artificial intelligence with real human care practices.
“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’s needs.”
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’s capabilities.
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.
“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.”
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.
