Articulation modeling enables robots to learn joint parameters of articulated objects for effective manipulation which can then be used downstream for skill learning or planning. Existing approaches often rely on prior knowledge about the objects, such as the number or type of joints. Some of these approaches also fail to recover occluded joints that are only revealed during interaction. Others require large numbers of multi-view images for every object, which is impractical in real-world settings. Furthermore, prior works neglect the order of manipulations, which is essential for many multi-DoF objects where one joint must be operated before another, such as a dishwasher. We introduce PokeNet, an end-to-end framework that estimates articulation models from a single human demonstration without prior object knowledge. Given a sequence of point cloud observations of a human manipulating an unknown object, PokeNet predicts joint parameters, infers manipulation order, and tracks joint states over time. PokeNet outperforms existing state-of-the-art methods, improving joint axis and state estimation accuracy by an average of over 27% across diverse objects, including novel and unseen categories. We demonstrate these gains in both simulation and real-world environments.

翻译：铰接建模使机器人能够学习铰接物体的关节参数以实现有效操作，这些参数随后可用于下游技能学习或规划。现有方法通常依赖于对物体的先验知识，例如关节数量或类型。其中部分方法无法恢复仅在交互过程中才显现的被遮挡关节。另一些方法则需要对每个物体采集大量多视角图像，这在现实场景中并不实用。此外，先前研究忽略了操作顺序的重要性，而这对于许多多自由度物体（如洗碗机）至关重要——此类物体常需按特定顺序操作关节。本文提出PokeNet，一种无需物体先验知识、仅通过单次人类演示即可估计铰接模型的端到端框架。给定人类操作未知物体时的点云观测序列，PokeNet能够预测关节参数、推断操作顺序并持续跟踪关节状态。PokeNet在多样化物体（包括新颖和未见类别）上的性能优于现有最先进方法，关节轴与状态估计精度平均提升超过27%。我们在仿真和真实环境中均验证了这些优势。