Real-IKEA：物理保真度是鲁棒操作的前提
Real-IKEA: Physical Fidelity is the Prerequisite for Robust Manipulation
AI总结：
针对仿真与现实物理差异导致操作鲁棒性不足的问题，提出Real-IKEA数据集与仿真框架，通过高保真资产和阻力校准配置，使强化学习策略发现优先利用机械优势的鲁棒策略。
摘要：
机器人操作的鲁棒性常常因简化仿真与充满阻力的现实世界之间的物理差距而失败。在这项工作中，我们强调在铰接交互中的物理真实性是鲁棒策略学习的重要因素。我们提出了Real-IKEA，一个以物理精度为首要目标的数据集和仿真框架。Real-IKEA提供了1,079个铰接资产配置，源自83个真实的IKEA把手和旋钮，经过细致的六步物理工作流程处理。对于接触几何精度，我们引入了一个双向表面偏差度量来量化碰撞网格。对于动力学真实性，我们建立了阻力校准配置，改变阻尼和摩擦。关键的是，我们通过强化学习策略证明，高保真资产能够发现鲁棒的“钩”和“杠杆”策略，这些策略优先考虑机械优势而非脆弱的摩擦拉动。总之，这些结果使Real-IKEA成为开发能够在铰接物体任务中达到人类水平鲁棒性的操作策略的关键基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
