ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K
This addresses the problem of limited simulation data for robotic manipulation researchers, though it is incremental as it builds on existing simulation-based learning paradigms.
The paper tackles the lack of data-generation-ready digital assets for robotic manipulation simulation by presenting ManiTwin, an automated pipeline that transforms single images into simulation-ready 3D assets, resulting in a dataset of 100K high-quality annotated assets.
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/.