AdaAfford：通过少样本交互学习适配3D铰接物体的操作可供性
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
AI总结：
针对现有3D铰接物体操作可供性学习方法忽略隐藏运动学与动力学因素导致精度下降的问题，提出AdaAfford框架，通过测试时少量交互适配可供性先验，在PartNet-Mobility数据集上表现优于基线。
摘要：
感知并与橱柜、门、水龙头等3D铰接物体交互，对未来在人类环境中执行日常任务的家用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，研究人员近来主张在输入形状几何上学习操作可供性，这种方式更具任务感知性且几何粒度更细。然而，这类方法仅将被动观测作为输入，忽略了诸多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），因此在存在此类不确定性的测试用例上准确性大幅下降。本文提出了一个名为AdaAfford的新颖框架，该框架学习在测试时执行极少量交互，以快速将可供性先验适配为更准确的实例特定后验。我们使用PartNet-Mobility数据集开展了大规模实验，证明我们的系统性能优于各基线方法。
英文摘要：
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.
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。
