An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfaces by leveraging differential mathematical operators on discrete manifolds to embed information on the geometry of the workpiece extracted from triangular meshes, and extend the Dynamic Movement Primitives (DMPs) framework to generate motions on the mesh surfaces. We also propose an effective strategy to adapt the motion to different surfaces, by introducing an isometric transformation of the learned forcing term. The resulting approach, namely MeshDMP, is evaluated both in simulation and real experiments, showing promising results in typical industrial automation tasks like car surface polishing.

翻译：工业自动化中的一个开放性问题是如何可靠地执行需要与复杂工件进行接触式运动的任务，因为现有解决方案缺乏无缝适应工件几何形状的能力。本文提出一种基于示教学习的方法，使机器人操作臂能够学习并泛化复杂曲面上的运动。该方法利用离散流形上的微分数学算子嵌入从三角网格提取的工件几何信息，并扩展动态运动基元框架以在网格表面生成运动。我们还提出一种有效的运动自适应策略，通过引入对学习到的强制项的等距变换来适应不同曲面。所提出的方法（称为MeshDMP）在仿真和真实实验中均进行了评估，在汽车表面抛光等典型工业自动化任务中显示出良好的应用前景。