Equivariant Diffusion Policy (2407.01812v3)

Abstract: Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this work, we propose Equivariant Diffusion Policy, a novel diffusion policy learning method that leverages domain symmetries to obtain better sample efficiency and generalization in the denoising function. We theoretically analyze the $\mathrm{SO}(2)$ symmetry of full 6-DoF control and characterize when a diffusion model is $\mathrm{SO}(2)$-equivariant. We furthermore evaluate the method empirically on a set of 12 simulation tasks in MimicGen, and show that it obtains a success rate that is, on average, 21.9% higher than the baseline Diffusion Policy. We also evaluate the method on a real-world system to show that effective policies can be learned with relatively few training samples, whereas the baseline Diffusion Policy cannot.

• The paper introduces Equivariant Diffusion Policy, integrating symmetry into diffusion models for enhanced visuomotor control.
• It demonstrates improved sample efficiency and a 21.9% success rate boost in robotic manipulation using limited demonstrations.
• The method generalizes across 6-DoF control tasks and paves the way for incorporating additional symmetry groups in robotic AI.

Equivariant Diffusion Policy: An Advanced Approach for Enhancing Visuomotor Policy Learning

The paper proposes a novel method termed "Equivariant Diffusion Policy," aimed at enhancing the efficacy of diffusion models in behavior cloning, a key avenue in robotic manipulation tasks. This method introduces an intriguing strategy to integrate domain symmetries directly into the learning process, specifically targeting the equivariance properties within the denoising function of diffusion models.

Theoretical Contributions and Methodological Advances

The paper bravely explores the underlying symmetry of large-scale visuomotor control tasks, leveraging the $\mathrm{SO}(2)$ symmetry group within a 6-DoF control framework. The authors meticulously articulate the conditions under which a diffusion model exhibits $\mathrm{SO}(2)$-equivariance, which stands as a salient theoretical contribution of the paper. The central theoretical proposition establishes that the noise prediction function behaves equivariantly when the expert policy itself is equivariant.

The authors extend this line of reasoning by articulating how $SE(3)$ action spaces can be imbued with $SO(2)$-equivariance, challenging prior methodologies constrained to a less expressive $SE(2)$ space. This theoretical framing allows for a more refined and theoretically grounded approach to using diffusion models within robotic manipulation, potentially enhancing both sample efficiency and generalization.

Experimental Validation and Empirical Outcomes

To validate the proposed method, the authors conduct a comprehensive suite of experiments on a set of 12 manipulation tasks utilizing the MimicGen environment, alongside real-world robot evaluations. The results indicate a substantial improvement in performance, with an average success rate increment of 21.9% over baseline diffusion policies when trained with 100 demonstrations. Such performance underscores the model's enhanced sample efficiency and ability to generalize across diverse manipulation scenarios.

Furthermore, real-world experiments illuminate the practical applicability of the Equivariant Diffusion Policy. The proposed model showcases its competence in learning effective policies using as few as 20 to 60 demonstrations in varied manipulation tasks. These findings reinforce the theoretical claim regarding the benefits of incorporating domain symmetries into the diffusion process.

Implications and Future Directions

The potential implications of this work span both theoretical insights and practical advancements in AI-driven robotic manipulation. By embedding symmetry directly into the learning model, the research not only improves the computational efficiency but also hints at a more generalized method adaptable to a wider range of tasks beyond the specific cases examined in this paper.

A conceivable future direction might involve exploring the integration of additional symmetry groups and their representations, particularly in complex, real-world environments where noise and dynamic variables challenge the current state of robotic AI.

Moreover, while the paper excels in leveraging voxel-based observation representations to enhance symmetry alignment with the environment, there remains room for innovation in optimizing vision systems to mitigate the current symmetry-breaking factors.

In summary, the Equivariant Diffusion Policy presents a significant step forward in the field of policy learning, effectively utilizing the symmetry inherent in tasks to enhance both learning efficiency and policy robustness. Such advancements harbor the promise of propelling AI research toward more autonomous and adaptable robotic systems, equipped to seamlessly interact within their operational domains.