Learning grinding skills from human craftsmen via imitation learning has
become a key research topic in robotic machining. Due to their strong
generalization and robustness to external disturbances, Dynamical Movement
Primitives (DMPs) offer a promising approach for robotic grinding skill
learning. However, directly applying DMPs to grinding tasks faces challenges,
such as low orientation accuracy, unsynchronized position-orientation-force,
and limited generalization for surface trajectories. To address these issues,
this paper proposes a robotic grinding skill learning method based on geodesic
length DMPs (Geo-DMPs). First, a normalized 2D weighted Gaussian kernel and
intrinsic mean clustering algorithm are developed to extract geometric features
from multiple demonstrations. Then, an orientation manifold distance metric
removes the time dependency in traditional orientation DMPs, enabling accurate
orientation learning via Geo-DMPs. A synchronization encoding framework is
further proposed to jointly model position, orientation, and force using a
geodesic length-based phase function. This framework enables robotic grinding
actions to be generated between any two surface points. Experiments on robotic
chamfer grinding and free-form surface grinding validate that the proposed
method achieves high geometric accuracy and generalization in skill encoding
and generation. To our knowledge, this is the first attempt to use DMPs for
jointly learning and generating grinding skills in position, orientation, and
force on model-free surfaces, offering a novel path for robotic grinding.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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