从人类示范中学习多关节物体的运动模型，提升机器人操控精度。
Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects
- 用人类示范数据训练神经网络，捕捉多关节物体的运动顺序与约束。
- 在真实数据上，关节轴与状态估计准确率提升超20%。
- 适合需要精准操控复杂物体的机器人任务，如装配、翻转。
随着机器人在多样化环境中部署，需与具有多个独立关节或自由度（DoF）的复杂物体交互，要求精确控制。现有方法常依赖先验知识或仅处理单自由度物体，难以应对遮挡关节及操作序列问题。本文提出从人类示范中学习物体模型。引入物体运动序列机（OKSM），一种同时捕捉运动约束与操作顺序的新表示。为从点云数据中估计该模型，提出Pokenet，一种基于人类示范训练的深度神经网络。在8,000个仿真和1,600个真实世界标注样本上验证。Pokenet在真实数据上的关节轴与状态估计性能优于以往方法超过20%。最终，利用逆运动学规划，在Sawyer机器人上成功实现对多自由度物体的操作。
原文摘要 · Abstract (English)
As robots become more generalized and deployed in diverse environments, they must interact with complex objects, many with multiple independent joints or degrees of freedom (DoF) requiring precise control. A common strategy is object modeling, where compact state-space models are learned from real-world observations and paired with classical planning. However, existing methods often rely on prior knowledge or focus on single-DoF objects, limiting their applicability. They also fail to handle occluded joints and ignore the manipulation sequences needed to access them. We address this by learning object models from human demonstrations. We introduce Object Kinematic Sequence Machines (OKSMs), a novel representation capturing both kinematic constraints and manipulation order for multi-DoF objects. To estimate these models from point cloud data, we present Pokenet, a deep neural network trained on human demonstrations. We validate our approach on 8,000 simulated and 1,600 real-world annotated samples. Pokenet improves joint axis and state estimation by over 20 percent on real-world data compared to prior methods. Finally, we demonstrate OKSMs on a Sawyer robot using inverse kinematics-based planning to manipulate multi-DoF objects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。