Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation (2409.14411v2)

Abstract: Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep neural networks, typically suggesting that increasing model size would lead to enhanced performance. However, our observations indicate that Diffusion Policy in transformer architecture (\DP) struggles to scale effectively; even minor additions of layers can deteriorate training outcomes. To address this issue, we introduce Scalable Diffusion Transformer Policy for visuomotor learning. Our proposed method, namely \textbf{\methodname}, introduces two modules that improve the training dynamic of Diffusion Policy and allow the network to better handle multimodal action distribution. First, we identify that \DP~suffers from large gradient issues, making the optimization of Diffusion Policy unstable. To resolve this issue, we factorize the feature embedding of observation into multiple affine layers, and integrate it into the transformer blocks. Additionally, our utilize non-causal attention which allows the policy network to \enquote{see} future actions during prediction, helping to reduce compounding errors. We demonstrate that our proposed method successfully scales the Diffusion Policy from 10 million to 1 billion parameters. This new model, named \methodname, can effectively scale up the model size with improved performance and generalization. We benchmark \methodname~across 50 different tasks from MetaWorld and find that our largest \methodname~outperforms \DP~with an average improvement of 21.6\%. Across 7 real-world robot tasks, our ScaleDP demonstrates an average improvement of 36.25\% over DP-T on four single-arm tasks and 75\% on three bimanual tasks. We believe our work paves the way for scaling up models for visuomotor learning. The project page is available at scaling-diffusion-policy.github.io.

• The paper introduces ScaleDP to overcome diffusion policy scalability issues by stabilizing transformer training dynamics.
• It applies feature embedding factorization and non-causal attention to significantly improve performance on MetaWorld and real-world robotic tasks.
• Experimental results show a 21.6% average boost in MetaWorld tasks and remarkable gains in single-arm and bimanual robotic experiments.

Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation

The paper "Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation" investigates the scalability of Diffusion Policy within the transformer architecture, a critical step for enhancing end-to-end visuomotor robot control. The paper identifies significant limitations in the scalability of existing Diffusion Policies when encapsulated in transformer architectures and proposes a novel methodology, ScaleDP, to overcome these challenges.

Key Contributions and Methodology

The paper presents several key contributions:

1. Identification of Scalability Issues: The authors address the inefficacy of scaling Diffusion Policies in traditional transformer architectures, noting that even minor additions of layers can destabilize training due to large gradient issues.
2. Introduction of ScaleDP: ScaleDP incorporates two core modifications:
   • Feature Embedding Factorization: The new feature embedding strategy utilizes multiple affine layers to stabilize training dynamics by reducing large gradients.
   • Non-Causal Attention: Implementing non-causal attention allows the policy network to consider future actions during prediction, mitigating compounding errors.
3. Extensive Evaluation: The effectiveness of ScaleDP is demonstrated across 50 different tasks in MetaWorld and 7 real-world robotic tasks, achieving substantial improvements in performance and generalization over the baseline DP-T.

Experimental Evaluation

The empirical results substantiate the scalability and efficacy of ScaleDP. In MetaWorld, ScaleDP exhibits an average improvement of 21.6% over DP-T across various tasks. Real-world validations further emphasize the robustness of ScaleDP, with significant performance gains recorded: an average improvement of 36.25% for single-arm tasks and 75% for bimanual tasks.

Implications and Speculations

Practical Implications:

• Enhanced Robustness and Generalization: ScaleDP's ability to scale up to 1 billion parameters without training instability highlights its potential for real-world applications where robust performance in diverse environments and tasks is crucial.
• Broader Applicability: The integration of non-causal attention and feature embedding factorization could be extended to other machine learning domains reliant on transformer architectures, suggesting a pathway to scalable deep learning models beyond robotic control.

Theoretical Implications:

• Gradient Magnitude Insights: The correlation between model depth and gradient magnitude provides valuable insights into optimizing deep neural networks, informing future research on mitigating training instabilities.

Future Developments in AI:

• Scalability in AI Models: Future research could build on the findings of this paper to explore scalable architectures for other domains such as natural language processing and computer vision, potentially leading to the development of more generalized AI systems.
• Improved Learning Dynamics: Continued refinement of attention mechanisms and embedding strategies may further enhance the efficiency of learning from large-scale data, enabling more sophisticated and capable AI models.

Conclusion

This paper makes significant strides in solving the scalability issues of Diffusion Policies in transformer architectures, enabling training with an unprecedented increase in model size up to 1 billion parameters. ScaleDP's novel architecture and training methods present a substantial advancement in the field of robotic manipulation, with far-reaching implications for the scalability and robustness of AI systems across various applications. This work sets a foundation for future exploration into scalable models, promising improved performance and efficiency in diverse machine learning tasks.