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Stochastic Approach for Modeling a Soft Robotic Finger with Creep Behavior

Sumitaka Honji, Hikaru Arita, Kenji Tahara

TL;DR

The paper addresses the challenge of modeling soft fingers with creep and motion variability for model-based control. It introduces a lumped-parameter framework with 3-element viscoelastic joints and treats model parameters as probabilistic distributions, enabling stochastic analysis via random variable transformation. Analytical solutions for joint displacement under creep are used to estimate parameters and to derive PDFs and Sobol sensitivity indices, which closely match experimental results and quantify parameter influences over time. The approach provides a computationally efficient, uncertainty-aware model suitable for observers and control in dexterous soft robotics, with potential extensions to broader soft-robot architectures and other viscoelastic models.

Abstract

Soft robots have high adaptability and safeness which are derived from their softness, and therefore it is paid attention to use them in human society. However, the controllability of soft robots is not enough to perform dexterous behaviors when considering soft robots as alternative laborers for humans. The model-based control is effective to achieve dexterous behaviors. When considering building a model which is suitable for control, there are problems based on their special properties such as the creep behavior or the variability of motion. In this paper, the lumped parameterized model with viscoelastic joints for a soft finger is established for the creep behavior. Parameters are expressed as distributions, which makes it possible to take into account the variability of motion. Furthermore, stochastic analyses are performed based on the parameters' distribution. They show high adaptivity compared with experimental results and also enable the investigation of the effects of parameters for robots' variability.

Stochastic Approach for Modeling a Soft Robotic Finger with Creep Behavior

TL;DR

The paper addresses the challenge of modeling soft fingers with creep and motion variability for model-based control. It introduces a lumped-parameter framework with 3-element viscoelastic joints and treats model parameters as probabilistic distributions, enabling stochastic analysis via random variable transformation. Analytical solutions for joint displacement under creep are used to estimate parameters and to derive PDFs and Sobol sensitivity indices, which closely match experimental results and quantify parameter influences over time. The approach provides a computationally efficient, uncertainty-aware model suitable for observers and control in dexterous soft robotics, with potential extensions to broader soft-robot architectures and other viscoelastic models.

Abstract

Soft robots have high adaptability and safeness which are derived from their softness, and therefore it is paid attention to use them in human society. However, the controllability of soft robots is not enough to perform dexterous behaviors when considering soft robots as alternative laborers for humans. The model-based control is effective to achieve dexterous behaviors. When considering building a model which is suitable for control, there are problems based on their special properties such as the creep behavior or the variability of motion. In this paper, the lumped parameterized model with viscoelastic joints for a soft finger is established for the creep behavior. Parameters are expressed as distributions, which makes it possible to take into account the variability of motion. Furthermore, stochastic analyses are performed based on the parameters' distribution. They show high adaptivity compared with experimental results and also enable the investigation of the effects of parameters for robots' variability.
Paper Structure (18 sections, 18 equations, 9 figures, 1 table)

This paper contains 18 sections, 18 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem setting
  3. Related works
  4. Proposal
  5. Lumped parameterized modeling of a soft finger
  6. Design of the soft finger
  7. Dynamic model
  8. Viscoelastic constitutive equation
  9. Analytical solution of the joint displacement
  10. Joint behavior for step input
  11. Stochastic expression of viscoelastic parameters
  12. Experimental setup
  13. Parameter distribution
  14. Stochastic analysis
  15. Stochastic expression of joint angle transition
  16. ...and 3 more sections

Figures (9)

  • Figure 1: The geometry of the soft finger.
  • Figure 2: The model of a soft finger and the detail structure of joint.
  • Figure 3: Viscoelastic 3 elements model of joint $i$.
  • Figure 4: Abstract of experimental setup.
  • Figure 5: The histograms and the PDFs are shown. The row shows the result of viscoelatic parameters of a joint, and the column does that of the same element of every joint.
  • ...and 4 more figures