Heterogeneous object manipulation on nonlinear soft surface through linear controller

TL;DR

This paper presents a simple PID-based control method for manipulating heterogeneous objects on soft surfaces, avoiding complex training and enabling practical real-world applications.

Contribution

It introduces a geometric transformation-driven PID control strategy that simplifies control of high-DOF soft manipulation surfaces without extensive training.

Findings

Successfully manipulated diverse objects including fragile ones like eggs and apples.

Validated through simulations and physical experiments demonstrating robustness and generalization.

Achieved precise control with reduced complexity and training requirements.

Abstract

Manipulation surfaces indirectly control and reposition objects by actively modifying their shape or properties rather than directly gripping objects. These surfaces, equipped with dense actuator arrays, generate dynamic deformations. However, a high-density actuator array introduces considerable complexity due to increased degrees of freedom (DOF), complicating control tasks. High DOF restrict the implementation and utilization of manipulation surfaces in real-world applications as the maintenance and control of such systems exponentially increase with array/surface size. Learning-based control approaches may ease the control complexity, but they require extensive training samples and struggle to generalize for heterogeneous objects. In this study, we introduce a simple, precise and robust PID-based linear close-loop feedback control strategy for heterogeneous object manipulation on MANTA-RAY (Manipulation with Adaptive Non-rigid Textile Actuation with Reduced Actuation density). Our approach employs a geometric transformation-driven PID controller, directly mapping tilt angle control outputs(1D/2D) to actuator commands to eliminate the need for extensive black-box training. We validate the proposed method through simulations and experiments on a physical system, successfully manipulating objects with diverse geometries, weights and textures, including fragile objects like eggs and apples. The outcomes demonstrate that our approach is highly generalized and offers a practical and reliable solution for object manipulation on soft robotic manipulation, facilitating real-world implementation without prohibitive training demands.