Discretizing SO(2)-Equivariant Features for Robotic Kitting
CoRR(2024)
摘要
Robotic kitting has attracted considerable attention in logistics and
industrial settings. However, existing kitting methods encounter challenges
such as low precision and poor efficiency, limiting their widespread
applications. To address these issues, we present a novel kitting framework
that improves both the precision and computational efficiency of complex
kitting tasks. Firstly, our approach introduces a fine-grained orientation
estimation technique in the picking module, significantly enhancing orientation
precision while effectively decoupling computational load from orientation
granularity. This approach combines an SO(2)-equivariant network with a group
discretization operation to preciously predict discrete orientation
distributions. Secondly, we develop the Hand-tool Kitting Dataset (HKD) to
evaluate the performance of different solutions in handling
orientation-sensitive kitting tasks. This dataset comprises a diverse
collection of hand tools and synthetically created kits, which reflects the
complexities encountered in real-world kitting scenarios. Finally, a series of
experiments are conducted to evaluate the performance of the proposed method.
The results demonstrate that our approach offers remarkable precision and
enhanced computational efficiency in robotic kitting tasks.
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