WireCraft: A Simulation Benchmark for Industrial DLO Manipulation

• URL: http://arxiv.org/abs/2606.18097v1
• Date: Tue, 16 Jun 2026 15:59:46 GMT
• Title: WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
• Abstract summary: We introduce WireCraft, a simulation benchmark for industrial DLO manipulation with difficulty and assets.<n>It supports two complementary DLO physics models, articulated and deformable, and the trajectories come from both simulation and a physical UR5.<n>We benchmark reinforcement learning (RL), imitation learning (IL), and vision-language-action (VLA) policies under shared metrics.
• Score: 4.764010993599256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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. For connector insertion, however, the transition from reaching the socket to contact-rich alignment remains a key bottleneck for vision RL, IL, and VLA policies. These results indicate that industrial DLO manipulation, though tractable under privileged state, remains an open challenge for current vision-based learning. The benchmark, data, and tools will be open-sourced upon acceptance.

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