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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Learning Optimal Fin-Ray Finger Design for Soft Grasping.

Zhifeng Deng¹, Miao Li^1,2

¹Learning Algorithms and Soft Manipulation Laboratory, The Institute of Technological Science, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China.

Frontiers in Robotics and AI

|March 1, 2021

Summary

This summary is machine-generated.

This study introduces a framework for optimizing soft robotic hand design, enabling stable grasping with reduced control complexity. The approach learns optimal parameters for fin-ray fingers, validated through simulations and real-world experiments.

Keywords:

grasp quality grasp quality criterion robotic grasping soft finger design soft hands

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Area of Science:

Robotics
Mechanical Engineering
Artificial Intelligence

Background:

Soft robotic hands offer passive compliance, simplifying control for grasping tasks.
Optimal design of soft fingers for specific tasks remains an open challenge.

Purpose of the Study:

To propose a framework for learning optimal design parameters of fin-ray soft fingers.
To achieve stable robotic grasping through optimized finger design.

Main Methods:

Simulated pseudo-kinematics learning for soft fingers.
Encoding task constraints using grasping force and a winding number-based quality function.
Experimental validation in simulation and real-world scenarios.

Main Results:

A framework for optimizing soft finger design parameters was developed.
The approach demonstrated effectiveness in achieving stable grasping.
Validated through both simulated and physical robotic systems.

Conclusions:

The proposed framework enables the design of optimal soft fingers for stable grasping.
This work advances robotic grasping by integrating passive compliance and intelligent design.
The method is effective for both simulated and real-world robotic applications.