Temporally-Masked Diffusion: An Effective Behavioral Cloning Method in Robot Action Sequence Generation

Diffusion models have shown significant potential in imitation learning, particularly for modeling complex data distributions. However, challenges remain in effectively capturing temporal dependencies in sequential decision-making tasks that are crucial for accurately representing the dynamics of actions and predicting long-term outcomes. This paper proposes temporally-masked diffusion (TM-Diff), an innovative approach that enhances the learning of essential temporal dependencies within action sequences. The proposed TM-Diff introduces a temporal masking method, masking certain actions at the time-step level during training. A novel time-series diffusion Transformer is designed to reconstruct the masked positions as a self-supervised learning auxiliary task, while still following the diffusion training process. This enables TM-Diff to infer missing action information, allowing it to learn temporal relationships between action sequence tokens. The effectiveness of TM-Diff is validated across six complex manipulation tasks. Experimental results indicate that TM-Diff demonstrates improved generative performance compared to standard diffusion-based policies and traditional observation-to-action methods. Particularly in small-sample demonstration datasets, TM-Diff achieves notable success, with a 6.2% improvement in the success rate for the Square task, and a 10.1% improvement in the target coverage ratio for the Push-T task, using only 50 demonstration samples. As a reliable and robust task execution policy, the proposed TM-Diff supports the responsible deployment of intelligent robotic systems in critical domains such as the Industrial Internet of Things (IIoT), home services, healthcare and logistics.