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

2025-02-20

AI Summary

Modeling and reliable manipulation of branched deformable linear objects (BDLOs)—such as wire harness branches—remain challenging in automated wire harness assembly due to complex dynamic propagation, bifurcation-point force coupling, and non-terminal grasping dynamics. Method: We propose the first differentiable branched elastic rod model, unifying these phenomena within a single physically grounded framework. Our approach integrates a differentiable discrete elastic rod physics engine, graph-structured dynamics modeling, neural-assisted parameter calibration, and a gradient-driven motion planning pipeline, enabling millisecond-scale real-time prediction (<10 ms) while preserving physical consistency. Contribution/Results: Experiments demonstrate a 32% improvement in prediction accuracy over state-of-the-art methods, with strong generalization to unseen BDLO topologies. The method successfully enables complex tasks—including wire harness insertion and pole-wrapping—on a real robotic platform.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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/.

Problem

• Modeling complex branched cable dynamics
• Enabling real-time manipulation of BDLOs
• Combining physics and learning for accurate predictions

Innovation

• Differentiable physics-based model
• Real-time BDLO dynamics modeling
• Combined learning and physics framework