WireCraft: A Simulation Benchmark for Industrial DLO Manipulation

arXiv:2606.18097v2 Announce Type: replace Abstract: 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.

Abstract: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.

Accepted at CoRL 2026 (Spotlight). Camera-ready version with additional simulation and real-robot experiments. 29 pages including references and appendices

Robotics (cs.RO)

arXiv:2606.18097 [cs.RO]

(or arXiv:2606.18097v2 [cs.RO] for this version)

aiqbench analysis

WireCraft: A Simulation Benchmark for Industrial DLO Manipulation

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