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

Doctorate

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

Kevin Dai

Post-Doctorate

Research Interests
bio-robots and controls
Ravesh Sukhnandan headshot

Ravesh Sukhnandan

Doctorate

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

Yu Wang

Master's

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

CWRU research team

Roger Quinn headshot

Roger Quinn

Faculty

Hillel Chiel

Hillel Chiel

Faculty

Yanjun Li headshot

Yanjun Li

Doctorate

Research highlights

  • Viscoelastic McKibben actuators
    Tuning materials makes pneumatic muscles more biomimetic
  • Images of training results for a synthetic nervous system
    SNS discretization allows backprop and parameter learning
  • Images of training results for a synthetic nervous system
    SNS discretization allows backprop and parameter learning
  • An SNS controller architecture for a grasping robot
    SNS controllers can be developed inspired by animal nervous systems
  • Results from an SNS controlled gantry robot
    SNS control translates from sim-to-real with minimal tuning
  • Results from an SNS controlled soft grasping robot
    SNS control and tunable stiffness in soft grasping allows a range of objects to be pick-and-placed
  • Picture of PhD student training undergraduate
    This research is broadening participation in robotics through undergraduate research
  • A schematic of an SNS neuron; results from simulations with adapting SNS control of a soft robot; results from real-world experiments of an adapting SNS controlled robot.
    The addition of bioinspired neuromodulation to SNS control improves soft robot performance for pick and place tasks.

    • 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, H.J. Chiel, V. A. Webster-Wood, R.D. Quinn,“Modulation and Time-history-dependent Adaptation Improves the Pick-and-Place Control of a Bioinspired Soft Grasper ” Living Machines 2024.
    2. R. Sukhnandan et al., “Synthetic Nervous System Control of a Bioinspired Soft Grasper for Pick-and-Place Manipulation,”. Living Machines 2024.
    3. 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.
    4. 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.