Collaborative Research: FRR: Adaptive mechanics, learning and intelligent control improve soft robotic grasping

Handling soft, fragile, or slippery objects such as ripe fruit remains a challenge in robotics. Soft robotic graspers show tremendous promise in safely handling such objects without damaging them. Furthermore, creating software to control soft robots poses an additional challenge.

In contrast, many animals with soft bodies solve this problem everyday as they forage and feed. Not only are they able to grasp and manipulate soft and fragile objects, but animals can also learn how to safely interact with new objects and vary how much force they apply during grasping based on their prior experience. This project will take inspiration from an animal with a body, the sea slug, that feeds successfully on a range of seaweeds that vary greatly in size, toughness and shape, to create new type of soft grasping robot.

This project will also create a mechanism that can learn how to safely grasp a wide range of objects, including fragile foods like tomatoes and mushrooms. The ability for a robot to learn how to safely handle soft and fragile objects will have future applications in agriculture, manufacturing, and medicine. This project will also support the training of a diverse workforce in science and engineering.

Students from grade school through college will be included as research participants to test the robot. Additionally, this project will support cross-disciplinary training through graduate student training, outreach activities, summer research experiences for undergraduates, and internships in scientific illustration.

This project will test the hypothesis that soft, morphologically intelligent grasping robots with onboard bioinspired learning and local control will improve grasping performance and ease of use by rapidly adjusting controller and actuator properties and learning in real-time. To test this hypothesis, this project will: (1) implement actuator adaptability over short timescales, mimicking short-term changes in biological muscle, (2) implement local control adaptability through short-term learning in a synthetic nervous system (SNS), mimicking short-term network changes in biological neural systems, and (3) implement longer-term synaptic weight changes in an SNS, mimicking learning from experience.

In Aims 1 and 2, a bioinspired approach will be applied to develop a soft grasper inspired by Aplysia californica (sea slug) feeding. In Aim 3, this approach will be extended to a robot arm and long-term learning will be incorporated into the controller. To precisely identify elements of the network subject to learning, this project will study grasping in a tractable animal model, Aplysia californica.

This marine sea slug is adept at grasping soft, fragile, slippery objects and rapidly learns with experience. Furthermore, Aplysia’s grasping control circuitry contains only a few hundred neurons, allowing the measurement of specific changes in key network elements during learning. To assess the value of biological principles for grasping, this project will use human subjects to measure the robotic grasper’s performance, ease of use, and operator training time.

Baseline data will be established with a conventional grasper and performance will be compared as adaptability is integrated into the system.

This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.