Robotic manipulation robustness often founders on the physics gap between simplified simulations and the resistance-laden real world. In this work, we emphasize that physical realism in articulated interaction is an important ingredient for robust policy learning. We present Real-IKEA, a dataset and simulation framework designed with physical accuracy as a first-class goal. Real-IKEA provides 1,079 articulated asset configurations, derived from 83 authentic IKEA handles and knobs processed through a meticulous six-step physical workflow. For contact-geometry accuracy, we introduce a bidirectional surface-deviation metric to quantify collision meshes. For dynamics realism, we establish resistance-calibrated configurations that vary damping and friction. Crucially, we demonstrate through a Reinforcement Learning (RL) policy that high-fidelity assets enable the discovery of robust "hooking" and "levering" strategies that prioritize mechanical advantage over fragile friction-pulling. Together, these results position Real-IKEA as a critical benchmark for developing manipulation policies capable of human-level robustness in articulated object tasks.

翻译：机器人操作鲁棒性常因简化仿真与充满阻力的现实世界之间的物理鸿沟而失效。本文强调，铰接交互中的物理真实性是鲁棒策略学习的重要基础。我们提出Real-IKEA——一个以物理精度为首要目标的数据集与仿真框架。该框架提供1,079个铰接式资产配置，源自83个真实宜家把手和旋钮，并通过严密的六步物理工作流程处理。针对接触几何精度，我们引入双向表面偏差度量来量化碰撞网格；针对动力学真实性，我们建立了校准阻力配置以改变阻尼和摩擦力。关键的是，通过强化学习策略，我们证明高保真资产能够发现优先考虑机械优势而非脆弱摩擦拉拽的稳健"勾取"和"撬动"策略。综上，这些结果将Real-IKEA定位为开发能在铰接物体任务中达到人类级鲁棒性的操作策略的关键基准。