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arXiv 2602.02741cs.RO
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PokeNet: 从人类观察中学习机械结构模型

PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations

School of Computation and AI, ASU, Tempe（计算与人工智能学院，亚利桑那州立大学）；AI Institute, Seoul National University, Seoul, South Korea（首尔国立大学人工智能研究所）

Anmol Gupta, Weiwei Gu, Omkar Patil, Jun Ki Lee, Nakul Gopalan

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AI总结：

PokeNet通过单个人类示范学习机械结构模型，无需先验知识，提升关节轴和状态估计精度达27%。

摘要：

机械结构建模使机器人能够学习机械臂的关节参数以实现有效的操作，这些参数可以用于后续的技能学习或规划。现有方法往往依赖于物体的先验知识，例如关节的数量或类型。一些方法还无法恢复被遮挡的关节，这些关节只有在交互时才被揭示。其他方法需要为每个物体提供大量的多视角图像，这在现实环境中是不切实际的。此外，先前的工作忽略了操作顺序，这对于许多多自由度物体至关重要，其中某个关节必须在另一个之前操作，例如洗碗机。我们介绍了PokeNet，这是一种端到端框架，能够从单个人类示范中估计机械结构模型。给定一个人类操作未知物体的点云观测序列，PokeNet预测关节参数，推断操作顺序，并在时间上跟踪关节状态。PokeNet在现有最先进方法上表现更优，平均提高了27%以上的关节轴和状态估计精度，涵盖了多样化的物体，包括新类别和未见过的类别。我们在模拟和现实环境中展示了这些改进。

英文摘要：

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.

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。

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