DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time

• URL: http://arxiv.org/abs/2502.15037v4
• Date: Thu, 06 Mar 2025 18:50:30 GMT
• Title: DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time
• Authors: Yizhou Chen, Xiaoyue Wu, Yeheng Zong, Anran Li, Yuzhen Chen, Julie Wu, Bohao Zhang, Ram Vasudevan,
• Abstract summary: Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT)<n>This paper presents Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT)
• Score: 10.058145238101655
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous wire harness assembly requires robots to manipulate complex branched cables with high precision and reliability. A key challenge in automating this process is predicting how these flexible and branched structures behave under manipulation. Without accurate predictions, it is difficult for robots to reliably plan or execute assembly operations. While existing research has made progress in modeling single-threaded Deformable Linear Objects (DLOs), extending these approaches to Branched Deformable Linear Objects (BDLOs) presents fundamental challenges. The junction points in BDLOs create complex force interactions and strain propagation patterns that cannot be adequately captured by simply connecting multiple single-DLO models. To address these challenges, this paper presents Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT), a novel framework that combines a differentiable physics-based model with a learning framework to: 1) accurately model BDLO dynamics, including dynamic propagation at junction points and grasping in the middle of a BDLO, 2) achieve efficient computation for real-time inference, and 3) enable planning to demonstrate dexterous BDLO manipulation. A comprehensive series of real-world experiments demonstrates DEFT's efficacy in terms of accuracy, computational speed, and generalizability compared to state-of-the-art alternatives. Project page:https://roahmlab.github.io/DEFT/.

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