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Embodying Control in Soft Multistable Robots from Morphofunctional Co-design.

Juan C Osorio1, Jhonatan S Rincon1, Harith Morgan1

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to control soft robots by co-designing their shape and tasks. This approach simplifies control for flexible robots, enabling them to adapt and perform complex actions with programmed dynamics.

Keywords:
embodied controlinverse designmechanical intelligencemodelingmultistability

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Area of Science:

  • Robotics
  • Materials Science
  • Control Systems

Background:

  • Soft robots offer unique flexibility and adaptability for tasks challenging for rigid robots.
  • Controlling soft robots is complex due to nonlinear material properties and infinite degrees of freedom.

Purpose of the Study:

  • To present a strategy for co-designing tasks and morphology in pneumatically actuated soft robots.
  • To enable programmed dynamics and multiple stable states for enhanced robot behavior.
  • To simplify soft robot control by integrating mechanical intelligence.

Main Methods:

  • Discretizing the infinite-dimensional configuration space into functional modes with programmed dynamics.
  • Developing an energy-based analytical model to capture soft robot responses.
  • Utilizing Recursive Feature Elimination to obtain model parameters.
  • Co-designing robot morphology (stiffness) and tasks (kinematics, time responses) using a lumped-parameter model.

Main Results:

  • Demonstrated inverse co-design of morphology and tasks for specific dynamics.
  • Created soft robots capable of classifying object size and weight.
  • Achieved adaptable locomotion with minimal feedback control.
  • Explored the configuration space by optimizing stiffness and time responses.

Conclusions:

  • The proposed strategy simplifies soft robot control by leveraging multistable structures.
  • Embodying mechanical intelligence into soft materials is a viable approach for advanced robotic capabilities.
  • This framework facilitates the design of adaptable and intelligent soft robotic systems.