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

TL;DR

MaskPlanner is a data-driven deep learning framework that generates object-centric motion trajectories from 3D point clouds, achieving high coverage and expert-level quality in industrial robotic tasks without relying on heuristics or restrictive assumptions.

Contribution

The paper introduces MaskPlanner, a novel neural network that predicts local path segments and groups them into paths directly from 3D point clouds, enabling scalable and adaptable motion planning.

Findings

Achieves over 99% coverage on unseen objects.

Produces trajectories that are directly executable on real robots.

Demonstrates expert-level painting quality in real-world tests.

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.