Allison Okamura, a Stanford University engineering professor, discusses human-centered robotics, haptic feedback, wearable devices, and soft robots for medicine, learning, and hazardous environments with Robot Talk host Claire Asher.
Okamura’s CHARM Lab works on robotics for surgery, rehabilitation, and support for older adults. One project explores an AI design assistant that helps people develop new soft and continuum robots through hands-on interaction. The aim is to find useful new mechanisms, including small, dexterous tools for surgery, while keeping human judgment part of the design process.
Okamura describes a heart-printing system that coordinates many flexible print heads to place living cells quickly, with a target of finishing in under an hour. It combines robotics methods such as planning and control with a specialized machine built for that job. She argues that different tasks call for different combinations of rigid and flexible parts.
Human touch relies on many kinds of receptors, and current robot tactile sensors remain limited. For human feedback, desktop haptic devices face a trade-off between stable operation and faithfully reproducing contact forces. Okamura describes wearable approaches that convey information through skin stimulation, including skin-stretch devices and breathable knitted garments with pneumatic actuators.
Vine robots extend by turning a soft tube inside out, which lets them travel through winding pipes without dragging their bodies along the walls. They can tolerate some punctures because holes do not continually widen as the robot advances. Okamura’s group sees potential for infrastructure inspection and, with further work on steering and lifting, search and rescue.
Okamura’s team is exploring clothing that can assemble itself around someone, potentially helping people with limited dexterity dress or use rehabilitation devices. In education, haptic interfaces let learners feel changing spring and damping properties, while vibration patterns can support piano practice and early number learning. She emphasizes working with educators and other specialists to shape these applications.
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