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

Abstract
Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications—such as robotic spray painting and welding—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 trajectories. 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, a real-world validation on a 6-DoF specialized painting robot demonstrates that the generated paths are directly executable and yield expert-level paint quality. Our findings crucially highlight the potential of learning-based OCMG to reduce engineering overhead and seamlessly adapt to several industrial use cases.

Authored by Gabriele Tiboni, Raffaello Camoriano, and Tatiana Tammasi from Politecnico di Torino. This work was supported by EFORT, providing the authors with domain knowledge, object meshes, trajectory data, and access to specialized painting robot hardware for our real-world experimental evaluation.