The gripper wrapping around a succulent. (Image: Harvard Microrobotics Lab/Harvard SEAS)

Most of today’s robotic grippers rely on embedded sensors, complex feedback loops, or advanced machine learning algorithms, combined with the skill of the operator, to grasp fragile or irregularly shaped objects. But researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated an easier way.

Taking inspiration from nature, they designed a new type of soft robotic gripper that uses a collection of thin tentacles to entangle and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles, or filaments, are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap around objects and doesn’t require sensing, planning, or feedback control.

“With this research, we wanted to reimagine how we interact with objects,” said Kaitlyn Becker, former graduate student and postdoctoral fellow at SEAS, who is currently Assistant Professor of Mechanical Engineering at MIT. “By taking advantage of the natural compliance of soft robotics and enhancing it with a compliant structure, we designed a gripper that is greater than the sum of its parts and a grasping strategy that can adapt to a range of complex objects with minimal planning and perception.”

The gripper’s strength and adaptability come from its ability to entangle itself with the object it is attempting to grasp. The foot-long filaments are hollow, rubber tubes. One side of the tube has thicker rubber than the other, so when the tube is pressurized, it curls like a pigtail or like straightened hair on a rainy day.

The curls knot and entangle with each other and the object, with each entanglement increasing the strength of the hold. While the collective hold is strong, each contact is individually weak and won’t damage even the most fragile object. To release the object, the filaments are simply depressurized. be able to solve it. Helping the model understand and accomplish longer-horizon planning tasks, such as, how the dough will deform given the current tool, movements, and actions, is a next step for future work.

A close-up of the gripper’s filaments wrapping around an object. (Image: Harvard Microrobotics Lab/Harvard SEAS)

The researchers used simulations and experiments to test the efficacy of the gripper, picking up a range to grasp soft fruits and vegetables for agricultural production and distribution, delicate tissue in medical settings, even irregularly shaped objects in warehouses, such as glassware.

For more information, contact Leah Burrows at This email address is being protected from spambots. You need JavaScript enabled to view it.; 617-496-1351.