Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action. This transition is highly significant for industrial robotic manipulation, promising to free human workers from repetitive, dangerous daily labor. To benchmark and advance this capability, we introduce the Robotic Collaborative Assembly Assistance (RoCo) Challenge with a dataset towards simulation and real-world assembly manipulation. Set against the backdrop of human-centered manufacturing, this challenge focuses on a high-precision planetary gearbox assembly task, a demanding yet highly representative operation in modern industry. Built upon a self-developed data collection, training, and evaluation system in Isaac Sim, and utilizing a dual-arm robot for real-world deployment, the challenge operates in two phases. The Simulation Round defines fine-grained task phases for step-wise scoring to handle the long-horizon nature of the assembly. The Real-World Round mirrors this evaluation with physical gearbox components and high-quality teleoperated datasets. The core tasks require assembling an epicyclic gearbox from scratch, including mounting three planet gears, a sun gear, and a ring gear. Attracting over 60 teams and 170+ participants from more than 10 countries, the challenge yielded highly effective solutions, most notably ARC-VLA and RoboCola. Results demonstrate that a dual-model framework for long-horizon multi-task learning is highly effective, and the strategic utilization of recovery-from-failure curriculum data is a critical insight for successful deployment. This report outlines the competition setup, evaluation approach, key findings, and future directions for industrial EAI. Our dataset, CAD files, code, and evaluation results can be found at: https://rocochallenge.github.io/RoCo2026/.

翻译：具身人工智能（EAI）正在迅速发展，逐步颠覆以往从孤立感知到集成、连续行动的自主系统范式。这一转变对于工业机器人操作具有重要意义，有望将人类从重复、危险的日常劳动中解放出来。为了衡量并推动这一能力的发展，我们推出了机器人协同装配辅助（RoCo）挑战赛，并附带一个面向仿真与真实世界装配操作的数据集。该挑战赛以“以人为本的制造”为背景，聚焦于一项高精度行星齿轮箱装配任务，这是现代工业中一项要求严苛但极具代表性的操作。挑战赛基于我们在Isaac Sim中自主研发的数据采集、训练与评估系统，并利用双臂机器人进行真实世界部署，分为两个阶段进行。仿真阶段定义了细粒度的任务阶段，用于分步评分，以应对装配任务的长周期特性。真实世界阶段则使用物理齿轮箱组件和高质量遥操作数据集进行镜像评估。核心任务要求从零开始装配一个行星齿轮箱，包括安装三个行星齿轮、一个太阳齿轮和一个齿圈。挑战赛吸引了来自10多个国家的60多支团队、170多名参与者参加，并产生了高效的解决方案，其中最突出的是ARC-VLA和RoboCola。结果表明，用于长周期多任务学习的双模型框架非常有效，而策略性地利用“从失败中恢复”的课程数据是成功部署的关键洞见。本报告概述了竞赛设置、评估方法、主要发现以及工业EAI的未来方向。我们的数据集、CAD文件、代码和评估结果可在以下网址找到：https://rocochallenge.github.io/RoCo2026/。