ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K

TL;DR

ManiTwin introduces an automated pipeline for creating large-scale, diverse, and annotated digital object datasets, facilitating scalable robotic manipulation simulation and policy learning.

Contribution

We develop ManiTwin, a novel pipeline that efficiently generates and annotates 3D digital assets, enabling the creation of a large, high-quality dataset for robotic manipulation research.

Findings

ManiTwin-100K contains 100,000 high-quality annotated 3D assets.

The pipeline enables efficient synthesis and annotation of digital objects.

The dataset supports diverse manipulation tasks and scene synthesis.

Abstract

Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data-generation-ready digital object twins. Our pipeline transforms a single image into simulation-ready and semantically annotated 3D asset, enabling large-scale robotic manipulation data generation. Using this pipeline, we construct ManiTwin-100K, a dataset containing 100K high-quality annotated 3D assets. Each asset is equipped with physical properties, language descriptions, functional annotations, and verified manipulation proposals. Experiments demonstrate that ManiTwin provides an efficient asset synthesis and annotation workflow, and that ManiTwin-100K offers high-quality and diverse assets for manipulation data generation, random scene synthesis, and VQA data generation, establishing a strong foundation for scalable simulation data synthesis and policy learning. Our webpage is available at https://manitwin.github.io/.