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

TL;DR

DEFT introduces a differentiable physics-based model combined with learning techniques to accurately and efficiently simulate and manipulate complex branched deformable linear objects in real-time, facilitating advanced robotic assembly tasks.

Contribution

The paper presents DEFT, a novel framework that models branched deformable linear objects with high accuracy and real-time performance, addressing limitations of previous single-DLO models.

Findings

DEFT achieves superior accuracy in modeling BDLO dynamics.

The framework operates in real-time, enabling practical robotic manipulation.

Experimental results demonstrate improved generalizability over existing methods.

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/.