SWAG stands for Self-Wearing Adaptive Garment. The acronym is intentional: the suit dresses itself . Developed jointly by researchers at KAIST (Korea Advanced Institute of Science and Technology) and Stanford University's CHARM Lab, SWAG embeds soft, inflatable robotic tubes into the garment itself . When pressurized with air, these tubes act like growing vines — they extend, unfurl, and guide the fabric over the wearer's limbs and torso .
The mechanism is inspired by climbing ivy. Instead of dragging fabric across the body — which creates shear forces that can irritate skin — SWAG grows the garment outward and around the wearer, conforming to their posture even if they move . This unfurling-based deployment eliminates skin-garment friction and makes the process safe for people with sensitive skin or burn injuries .
The entire process is hands-free and takes roughly 10 seconds for a full suit . The system does not rely on advanced AI, cameras, or exact pose tracking — the soft robots adapt to the body shape in real time .
For people with disabilities, paralysis, or age-related weakness, pulling a shirt over the head or guiding an arm into a sleeve can be painful or impossible. Traditional dressing robots use rigid arms or mechanical grippers that pull fabric taut across the skin, creating pressure points and friction risks .
SWAG's key innovation is its zero-shear unfurling method. The garment inflates outward and wraps around the body rather than sliding across it, drastically reducing irritation . The system uses compliant, air-powered structures rather than rigid components, making the interaction inherently gentle . Users with limited mobility, muscle weakness, or paralysis can put on clothing autonomously without assistance .
According to the researchers, the system also works even when the wearer does not remain perfectly still, which is important for users with involuntary movements or tremors .
The project was led by Professor Jee-Hwan Ryu of the Department of Civil and Environmental Engineering at KAIST . The research was primarily conducted by Dr. Nam Gyun Kim of KAIST, who visited Stanford as part of the collaboration . On the Stanford side, Professor Allison Okamura and her team at the CHARM Lab (Collaborative Haptics and Robotics in Medicine) co-developed the technology .
The work built on earlier Stanford research into soft, vine-like growing robots capable of extending over 100 times their length, navigating tight spaces, and functioning as physical conduits .
SWAG was published in IEEE Robotics and Automation Letters (RA-L), one of the most selective journals in robotics . At ICRA 2025, the paper was chosen as one of only five winners of the RA-L Best Paper Award from more than 1,700 published papers — a 340-to-1 selection ratio .
Remarkably, Professor Ryu's team won the same award again in 2026 at ICRA 2026 in Vienna, Austria, making them the first team to win the RA-L Best Paper Award in two consecutive years . The team's work on soft growing robots received the 2025 award, and the SWAG paper earned the 2026 honor .
SWAG is still a research prototype, but the team has identified several high-impact use cases:
The technology is particularly promising for environments where workers must suit up multiple times per shift — a process that currently takes minutes of careful, contamination-avoiding effort .
The researchers emphasize that SWAG is still in the prototype stage and not yet commercially available . Current work focuses on scaling the system to different garment types, improving durability of the soft robotic vines, and testing with a wider range of body shapes and postures . The underlying soft growing robot technology has already been patented by Stanford for broader applications including search and rescue, medical tubing, and exploration .
For now, SWAG represents a fundamental shift in how robotic dressing can work — not by pulling clothing over the body, but by having the clothing grow around the wearer. It is a rare case where the acronym and the ambition match: the suit really does dress itself.