Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach

TL;DR

This paper introduces an efficient, adaptive, data-driven approach for learning global deformation models of elastic deformable linear objects, enabling precise large deformation control in robotic manipulation through offline training and online updating.

Contribution

It presents a coupled offline-online neural network-based model learning method with stability analysis and demonstrates superior large deformation control in real-world robotic tasks.

Findings

Accurately estimates deformation models for DLOs.

Achieves large deformation control in 2D and 3D tasks.

Successfully completes 24 real-world manipulation tasks.

Abstract

Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller, and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model, and achieve large deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning.