WireCraft：工业DLO操作仿真基准
WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
AI总结：
针对工业中可变形线性物体（DLO）操作缺乏统一基准的问题，提出WireCraft仿真基准，支持可配置难度和资产，涵盖三种任务族，并评估强化学习、模仿学习和视觉-语言-动作策略。
摘要：
可变形线性物体（DLO），如电线和电缆，是工业装配的核心。与刚体不同，刚体的状态由6自由度位姿捕获，而DLO具有无限维配置空间，并在与夹爪、夹具和工作空间的接触下连续变形，使其成为通用灵巧操作的一个高要求基准。尽管其重要性，策略开发和比较仍然困难：现有基准通常绑定到特定硬件设置，缺乏模块化和可定制的任务资产，或者研究没有真实世界工业线缆操作相关夹具的通用可变形物体任务。很少有基准将仿真、真实世界数据和共享评估协议对齐。为弥合这一差距，我们引入了WireCraft，一个用于工业DLO操作的仿真基准，具有可配置的难度和资产，涵盖三个任务族：连接器插入、夹子布线和通道就位。它支持两种互补的DLO物理模型——铰接式和可变形式，轨迹来自仿真和物理UR5。我们在共享指标下对强化学习（RL）、模仿学习（IL）和视觉-语言-动作（VLA）策略进行基准测试。基于特权状态的RL在每个任务族的一个代表性设置中实现了超过82%的成功率，确认了任务的良好定义。然而，对于连接器插入，从到达插座到接触丰富的对齐的过渡仍然是视觉RL、IL和VLA策略的关键瓶颈。这些结果表明，工业DLO操作虽然在特权状态下可处理，但对于当前基于视觉的学习仍然是一个开放的挑战。基准、数据和工具将在接收后开源。
英文摘要：
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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