Real-IKEA用真实物理数据提升机器人抓取鲁棒性，让机械优势胜过依赖摩擦。
Real-IKEA: Physical Fidelity is the Prerequisite for Robust Manipulation
- 构建高保真交互数据集，用6步物理流程还原83款宜家把手
- 通过双向表面偏差度量和阻尼/摩擦校准提升碰撞与动力学真实度
- 强化学习证明：高保真环境能发现更可靠的撬动、钩挂策略
机器人抓取的鲁棒性常因仿真与现实间的物理差距而受挫。本文强调，刚性物体交互中的物理真实性是鲁棒策略学习的关键。我们提出Real-IKEA数据集与仿真框架，以物理准确性为核心目标。该数据集包含1,079个刚性构件配置，源于83款真实宜家把手与旋钮，经六步物理流程处理。为提升接触几何精度，引入双向表面偏差度量以量化碰撞网格；为增强动力学真实性，建立阻尼与摩擦校准的配置。关键实验表明，高保真资产使强化学习策略成功发现依赖机械优势而非脆弱摩擦力的“钩挂”与“撬动”策略。这些结果使Real-IKEA成为实现人类级鲁棒性的刚性物体操作任务的重要基准。
原文摘要 · Abstract (English)
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.
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