Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments. Besides parsing the articulated parts and joint parameters, researchers recently advocate learning manipulation affordance over the input shape geometry which is more task-aware and geometrically fine-grained. However, taking only passive observations as inputs, these methods ignore many hidden but important kinematic constraints (e.g., joint location and limits) and dynamic factors (e.g., joint friction and restitution), therefore losing significant accuracy for test cases with such uncertainties. In this paper, we propose a novel framework, named AdaAfford, that learns to perform very few test-time interactions for quickly adapting the affordance priors to more accurate instance-specific posteriors. We conduct large-scale experiments using the PartNet-Mobility dataset and prove that our system performs better than baselines.

翻译：感知并操控三维铰接物体（如橱柜、门窗、水龙头等）对于未来在人类环境中执行日常任务的居家辅助机器人构成了特殊挑战。除了解析铰接部件与关节参数外，研究者近来倡导基于输入形状几何结构学习操控可供性，这种方法更具任务感知性且几何粒度更精细。然而，由于仅以被动观测数据作为输入，这些方法忽略了大量隐藏但重要的运动学约束（如关节位置与限位）及动力学因素（如关节摩擦与恢复系数），因此在处理存在此类不确定性的测试案例时精度显著下降。本文提出一种名为AdaAfford的新型框架，该框架通过学习在测试阶段进行极少量交互，能够快速将可供性先验自适应为更精确的实例特异性后验。我们基于PartNet-Mobility数据集开展了大规模实验，结果表明本系统性能优于基线方法。