# WireCraft: A Simulation Benchmark for Industrial DLO Manipulation

arXiv: https://arxiv.org/abs/2606.18097 · HTML: https://mlsanity.com/paper/2606.18097/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [RouteRLT: Learning When and Which RL Specialist Should Control a Vision-Language-Action Policy](https://mlsanity.com/paper/2609.26467/) (2026)

## Abstract (arXiv)

Deformable Linear Objects (DLOs), such as wires and cables, are central to industrial assembly. Unlike rigid objects, whose state is captured by a 6-DoF pose, DLOs have an infinite-dimensional configuration space and deform continuously under contact with grippers, fixtures, and the workspace, making them a demanding benchmark for general dexterous manipulation. Despite their importance, policy development and comparison remain difficult: existing benchmarks are often tied to specific hardware setups, lack modular and customizable task assets, or study generic deformable-object tasks without the fixtures relevant to real-world industrial wire manipulation. Few benchmarks align simulation, real-world data, and shared evaluation protocols. To bridge this gap, we introduce WireCraft, a simulation benchmark for industrial DLO manipulation with configurable difficulty and assets, spanning three task families: connector insertion, clip routing, and channel seating. It supports two complementary DLO physics models, articulated and deformable, and the released trajectories come from both simulation and a physical UR5. We benchmark reinforcement learning (RL), imitation learning (IL), and vision-language-action (VLA) policies under shared metrics. Privileged state-based RL solves a representative setting in each task family with over 82% success, confirming the tasks are well-posed. Image-based policies remain less successful across the evaluated tasks, with ACT and DiT VLA falling below privileged State PPO. On a physical UR5, supplementing real demonstrations with simulated data improves success by 15-65 percentage points in the best-performing mixture for each task. These results indicate that industrial DLO manipulation, though tractable under privileged state, remains an open challenge for current vision-based learning.