Heterogeneous object manipulation on nonlinear soft surface through linear controller

2025-07-20

AI Summary

To address the challenges of high-dimensional control complexity, poor generalization, and reliance on black-box training when manipulating heterogeneous objects on nonlinear soft surfaces using dense actuator arrays, this paper proposes a geometric-transformation-driven linear PID closed-loop control method. The approach directly maps surface tilt angles (1D or 2D) to actuator commands via analytical geometry, eliminating data-driven modeling and offline training. Evaluated on the MANTA-RAY soft manipulation platform, the system stably handles fragile, heterogeneous objects—such as eggs and apples—with markedly differing shapes, masses, and material properties, demonstrating strong robustness and exceptional generalization. Deployment and maintenance costs are significantly reduced. The core innovation lies in replacing high-dimensional nonlinear learning with interpretable, low-parameter geometric relationships, thereby unifying precision, universality, and practicality in soft-surface manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Control high-DOF soft surfaces for object manipulation
• Reduce complexity in heterogeneous object handling
• Eliminate extensive training for robust PID control

Innovation

• PID-based linear close-loop feedback control
• Geometric transformation-driven actuator command mapping
• Reduced actuation density for simplified control