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

2025-02-26

AI Summary

Existing object-centric motion generation (OCMG) methods for industrial multi-objective long-horizon motion planning exhibit poor adaptability—relying on heuristic design, expensive optimization, or strong geometric assumptions—thus failing to generalize to real-world scenarios. Method: We propose the first end-to-end, point-cloud-driven OCMG framework that directly learns object-centered trajectories from unstructured 3D point clouds, without geometric priors or hand-crafted optimization. We introduce a novel path mask mechanism enabling joint local path segment generation and global path grouping in a single forward pass, unifying geometric awareness with task-level semantics. The architecture integrates local neighborhood feature extraction, path segment regression, and differentiable path clustering. Results: On real-world robotic spray-painting tasks, our method achieves >99% surface coverage on unseen objects; the generated 6-DoF trajectories execute directly on physical robots and yield expert-level coating quality.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Generates object-centric motion from 3D point clouds
• Predicts and groups local path segments efficiently
• Achieves expert-level quality in robotic applications

Innovation

• Deep learning for motion generation
• Path masks for segment grouping
• Executable trajectories from point clouds