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An Environment-Adaptive Position/Force Control Based on Physical Property Estimation

Tomoya Kitamura, Yuki Saito, Hiroshi Asai, Kouhei Ohnishi

TL;DR

This work tackles the problem of robotic action adaptability in the face of varying environmental impedance, proposing an impedance-matching approach that uses only two prerecorded motions to adapt position and force commands. By estimating physical properties through a spring–mass–damper model and leveraging a reaction force observer, the method computes environment-aware command values that align with the current impedance, enabling robust performance without machine learning. Compared to motion reproduction systems, the proposed approach significantly reduces data requirements and maintains stability by operating with existing stable controllers, achieving substantial reductions in position and force errors across interpolation and extrapolation scenarios. The results demonstrate practical benefits for rapid, data-efficient, environment-aware robot control with potential for broader applications in teaching, manipulation, and haptic-assisted tasks.

Abstract

The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position and force control, confirmed the superiority of the proposed method over existing motion reproduction system. The advantages of this method include the use of only two sets of motion data, significantly reducing the burden of data acquisition compared with machine-learning based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving the environmental adaptability of robots while simplifying the action generation method.

An Environment-Adaptive Position/Force Control Based on Physical Property Estimation

TL;DR

This work tackles the problem of robotic action adaptability in the face of varying environmental impedance, proposing an impedance-matching approach that uses only two prerecorded motions to adapt position and force commands. By estimating physical properties through a spring–mass–damper model and leveraging a reaction force observer, the method computes environment-aware command values that align with the current impedance, enabling robust performance without machine learning. Compared to motion reproduction systems, the proposed approach significantly reduces data requirements and maintains stability by operating with existing stable controllers, achieving substantial reductions in position and force errors across interpolation and extrapolation scenarios. The results demonstrate practical benefits for rapid, data-efficient, environment-aware robot control with potential for broader applications in teaching, manipulation, and haptic-assisted tasks.

Abstract

The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position and force control, confirmed the superiority of the proposed method over existing motion reproduction system. The advantages of this method include the use of only two sets of motion data, significantly reducing the burden of data acquisition compared with machine-learning based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving the environmental adaptability of robots while simplifying the action generation method.

Paper Structure

This paper contains 20 sections, 21 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Control methods
  4. Position control
  5. Force control
  6. Position/Force control
  7. MRS
  8. Physical Property Estimation
  9. Proposed Method
  10. Experimental Methods
  11. Experimental Setup
  12. Samples
  13. Control Methods
  14. Verification Method
  15. Experimental Results
  16. ...and 5 more sections

Figures (8)

  • Figure 1: Block diagram with MRS
  • Figure 2: Block diagram with proposed method
  • Figure 3: Flowchart illustrating the implementation steps of the proposed method.
  • Figure 4: Sequential photographs showing the gripping motion: initial open state, gripping, full grip, and release.
  • Figure 5: Sequential photographs showing the gripping motion: initial open state, gripping, full grip, and release.
  • ...and 3 more figures