Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects

Yizhou Chen, Yiting Zhang, Zachary Brei, Tiancheng Zhang, Yuzhen Chen, Julie Wu, Ram Vasudevan

Keywords: Deformable Linear Objects Modeling, Physics-Informed Learning, Differentiable Simulation

TL;DR: This paper proposes a novel framework that combines a differentiable physics-based model with learning framework to model DLOs accurately and in real-time.

Abstract: This paper addresses the task of modeling Deformable Linear Objects (DLOs), such as ropes and cables, during dynamic motion over long time horizons. This task presents significant challenges due to the complex dynamics of DLOs. To address these challenges, this paper proposes differentiable Discrete Elastic Rods For deformable linear Objects with Real-time Modeling (DEFORM), a novel framework that combines a differentiable physics-based model with a learning framework to model DLOs accurately and in real-time. The performance of DEFORM is evaluated in an experimental setup involving two industrial robots and a variety of sensors. A comprehensive series of experiments demonstrate the efficacy of DEFORM in terms of accuracy, computational speed, and generalizability when compared to state-of-the-art alternatives. To further demonstrate the utility of DEFORM, this paper integrates it into a perception pipeline and illustrates its superior performance when compared to the state-of-the-art methods while tracking a DLO even in the presence of occlusions. Finally, this paper illustrates the superior performance of DEFORM when compared to state-of-the-art methods when it is applied to perform autonomous planning and control of DLOs.

Video: https://www.youtube.com/watch?v=_wiSPTap9-g&t=6s

Website: https://roahmlab.github.io/DEFORM/

Code: https://github.com/roahmlab/DEFORM

Student Paper: yes

Submission Number: 212