Learn to Position -- A Novel Meta Method for Robotic Positioning
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Absolute positioning accuracy is a vital specification for robots. Achieving
high position precision can be challenging due to the presence of various
sources of errors. Meanwhile, accurately depicting these errors is difficult
due to their stochastic nature. Vision-based methods are commonly integrated to
guide robotic positioning, but their performance can be highly impacted by
inevitable occlusions or adverse lighting conditions. Drawing on the
aforementioned considerations, a vision-free, model-agnostic meta-method for
compensating robotic position errors is proposed, which maximizes the
probability of accurate robotic position via interactive feedback. Meanwhile,
the proposed method endows the robot with the capability to learn and adapt to
various position errors, which is inspired by the human's instinct for grasping
under uncertainties. Furthermore, it is a self-learning and self-adaptive
method able to accelerate the robotic positioning process as more examples are
incorporated and learned. Empirical studies validate the effectiveness of the
proposed method. As of the writing of this paper, the proposed meta search
method has already been implemented in a robotic-based assembly line for
odd-form electronic components.
