New shape-shifting robot design uses mechanical memory for motion

A research team at the University of Sheffield, led by Dr. Lin Cao, has developed a novel approach to soft robotics called Hysteresis-Assisted Shape Morphing (HasMorph) that leverages the mechanical phenomenon of hysteresis—where motion lags behind applied forces—to enable precise, shape-shifting motion with fewer motors. Traditionally, robotic dexterity has been pursued by adding more actuators, increasing complexity and control challenges. Instead, HasMorph uses hysteresis as a form of mechanical memory, allowing soft robots to remember previous shapes and execute complex movements using minimal actuation. This results in robots that are lighter, cheaper, and easier to operate, capable of producing billions of shapes with just two tendons controlling multiple bending sections. The team further combined HasMorph with a soft “growing” robot that extends and retracts from its tip, mimicking plant growth and enabling “follow-the-leader” navigation through tight or unpredictable environments. This hybrid robot can steer around obstacles and precisely navigate confined