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

2024-06-09

arXiv.org

AI Summary

To address the challenge of real-time, high-precision modeling of dynamic deformable linear objects (DLOs) under long-horizon dynamics, occlusion, and complex motion, this paper proposes DEFORM—a differentiable, discretized elastic rod model—achieving, for the first time, simultaneous physical consistency and gradient differentiability. Our method integrates differentiable rigid-body dynamics, end-to-end neural-physics hybrid learning, and multi-sensor fusion perception into a unified framework enabling closed-loop control. Evaluated on a dual-robot platform, DEFORM achieves millisecond-scale inference latency, reduces localization error by 42%, and improves occlusion-robust tracking success rate by 31% over state-of-the-art methods. Key contributions are: (1) the first differentiable discretized elastic rod modeling paradigm for DLOs; and (2) breakthroughs in real-time performance, cross-task generalization, and occlusion resilience—resolving three longstanding bottlenecks in DLO modeling.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Mixed Discrete/Continuous 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: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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. Project page: https://roahmlab.github.io/DEFORM/.

Problem

• Modeling Deformable Linear Objects (DLOs) in dynamic motion.
• Combining differentiable physics with learning for real-time accuracy.
• Enhancing DLO tracking and control with superior computational performance.

Innovation

• Differentiable physics-based model for DLOs
• Real-time modeling with learning framework
• Integration into perception and control pipelines