3D policy learning promises superior generalization and cross-embodiment transfer, but progress has been hindered by training instabilities and severe overfitting, precluding the adoption of powerful 3D perception models. In this work, we systematically diagnose these failures, identifying the omission of 3D data augmentation and the adverse effects of Batch Normalization as primary causes. We propose a new architecture coupling a scalable transformer-based 3D encoder with a diffusion decoder, engineered specifically for stability at scale and designed to leverage large-scale pre-training. Our approach significantly outperforms state-of-the-art 3D baselines on challenging manipulation benchmarks, establishing a new and robust foundation for scalable 3D imitation learning. Project Page: https://r3d-policy.github.io/
@article{arxiv.2604.15281,
title = {R3D: Revisiting 3D Policy Learning},
author = {Zhengdong Hong and Shenrui Wu and Haozhe Cui and Boyi Zhao and Ran Ji and Yiyang He and Hangxing Zhang and Zundong Ke and Jun Wang and Guofeng Zhang and Jiayuan Gu},
journal= {arXiv preprint arXiv:2604.15281},
year = {2026}
}