An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors

TL;DR

This paper presents an adaptive grasping force control strategy using LSTM-based generalized stiffness estimation, enabling robots to accurately and adaptively grasp objects with complex nonlinear and time-varying behaviors.

Contribution

It introduces the concept of generalized stiffness and an LSTM-based estimator, improving adaptive force tracking for nonlinear, time-varying objects in robotic grasping.

Findings

Achieves high precision in force tracking

Demonstrates better adaptability to complex objects

Reduces probing time for grasping

Abstract

Accurate grasp force control is one of the key skills for ensuring successful and damage-free robotic grasping of objects. Although existing methods have conducted in-depth research on slip detection and grasping force planning, they often overlook the issue of adaptive tracking of the actual force to the target force when handling objects with different material properties. The optimal parameters of a force tracking controller are significantly influenced by the object's stiffness, and many adaptive force tracking algorithms rely on stiffness estimation. However, real-world objects often exhibit viscous, plastic, or other more complex nonlinear time-varying behaviors, and existing studies provide insufficient support for these materials in terms of stiffness definition and estimation. To address this, this paper introduces the concept of generalized stiffness, extending the definition of stiffness to nonlinear time-varying grasp system models, and proposes an online generalized stiffness estimator based on Long Short-Term Memory (LSTM) networks. Based on generalized stiffness, this paper proposes an adaptive parameter adjustment strategy using a PI controller as an example, enabling dynamic force tracking for objects with varying characteristics. Experimental results demonstrate that the proposed method achieves high precision and short probing time, while showing better adaptability to non-ideal objects compared to existing methods. The method effectively solves the problem of grasp force tracking in unknown, nonlinear, and time-varying grasp systems, demonstrating the generalization capability of our neural network and enhancing the robotic grasping ability in unstructured environments.