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A patient recovering from a limb amputation won't use a prostheses that isn't comfortable. A person rehabbing from a stroke won't use a robotic exoskeleton if the mobility it grants doesn't allow them to perform everyday activities. And a diabetes patient won't use an insulin pump that doesn't deliver the appropriate dosage of medicine to control their blood sugar.

All of these challenges fall under a concept known as "human–robot interaction," a critical issue in robotic design and engineering. A new perspective from researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) delves into a potential approach to improve human–robot interaction.

The approach, known as "human-in-the-loop optimization" (HILO), explores the potential of combining individual patient data with machine learning algorithms as a way to fine-tune robotic design and improve results for specific performance metrics.

Patrick Slade, Assistant Professor of Bioengineering at SEAS, was first author on the Perspective, which was published in Nature.

"I hope this perspective can accelerate the development and effectiveness of human–robot interactions when developing robotic systems, especially for medical applications where these systems interact with people," Slade said.

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