MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds

• URL: http://arxiv.org/abs/2502.18745v1
• Date: Wed, 26 Feb 2025 01:39:25 GMT
• Title: MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds
• Authors: Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi,
• Abstract summary: Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications.<n>We propose MaskPlanner, a learning method that predicts local paths while a deep learning method for a given object in "path" masks.<n>Our findings crucially highlight the proposed learning method for OCMG to reduce engineering overhead and seamlessly adapt to several industrial use cases.
• Score: 11.72951300809094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications$\unicode{x2014}$such as robotic spray painting and welding$\unicode{x2014}$requiring efficient, scalable, and generalizable algorithms to plan multiple long-horizon trajectories over free-form 3D objects. However, existing solutions rely on specialized heuristics, expensive optimization routines, or restrictive geometry assumptions that limit their adaptability to real-world scenarios. In this work, we introduce a novel, fully data-driven framework that tackles OCMG directly from 3D point clouds, learning to generalize expert path patterns across free-form surfaces. We propose MaskPlanner, a deep learning method that predicts local path segments for a given object while simultaneously inferring "path masks" to group these segments into distinct paths. This design induces the network to capture both local geometric patterns and global task requirements in a single forward pass. Extensive experimentation on a realistic robotic spray painting scenario shows that our approach attains near-complete coverage (above 99%) for unseen objects, while it remains task-agnostic and does not explicitly optimize for paint deposition. Moreover, our real-world validation on a 6-DoF specialized painting robot demonstrates that the generated trajectories are directly executable and yield expert-level painting quality. Our findings crucially highlight the potential of the proposed learning method for OCMG to reduce engineering overhead and seamlessly adapt to several industrial use cases.

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