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 every day 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 create a mechanism that can learn how to safely grasp a wide range of objects, including fragile foods like tomatoes and mushrooms. The ability of a robot to learn how to safely handle soft and fragile objects will have future applications in agriculture, manufacturing, and medicine.

Funding Agency: NSF - Foundational Research in Robotics Program 

Project Period: 2/2022 - 1/2025

Abstract Page: NSF-2138923


CMU research team

Victoria Webster-Wood headshot

Victoria Webster-Wood

Associate Professor
Mechanical Engineering

Courtesy appointments
Biomedical Engineering, Robotics Institute

Michael Bennington headshot

Michael Bennington


Research Interests
biomechanics, neuromechanics, biorobotics, and computational modeling
Kevin Dai headshot

Kevin Dai


Research Interests
bio-robots and controls
Ravesh Sukhnandan headshot

Ravesh Sukhnandan


Research Interests
oft robots, bioinspired robots, muscle mechanics
placeholder headshot graphic

Yu Wang


Research Interests
bioinspired robotics, soft robotics, human-robot interaction

CWRU research team

Roger Quinn headshot

Roger Quinn


Hillel Chiel

Hillel Chiel


Yanjun Li headshot

Yanjun Li


Research highlights

Research videos

Peer-reviewed publications

* Indicates these authors contributed equally and may both list themselves as the first author of this work.

  1. Y. Li*, R. Sukhnandan*, J. P. Gill, H. J. Chiel, V. Webster-Wood, R. D. Quinn. “A Bioinspired Synthetic Nervous System Controller for Pick-and-Place Manipulation”. ICRA 2023.
  2. M. J. Bennington*, T. Wang*, J. Yin, S. Bergbreiter, C. Majidi, V. A. Webster-Wood, “Design and Characterization of Viscoelastic McKibben Actuators with Tunable Force-Velocity Curves.” RoboSoft 2023.