提出球面傅里叶空间的等变扩散策略，提升3D操作泛化能力。
SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space
- 在球面傅里叶空间中实现状态、动作与去噪过程的SE(3)等变性
- 20个仿真任务和5个真实机器人任务上显著优于基线方法
- 适合需要3D场景变换鲁棒性的机器人操控研究者
扩散策略能从人类演示中学习闭环操作策略，但在3D场景新布局下泛化能力差。为此，我们提出球面扩散策略（SDP），一种在3D场景变换下保持等变性的扩散策略。通过将状态、动作及去噪过程嵌入球面傅里叶空间，实现SE(3)等变性。引入新颖的球面FiLM层，使动作去噪过程能等变地依赖场景嵌入。同时设计球面去噪时间U-Net，实现高效的空间-时间等变性。最终，SDP为端到端的SE(3)等变策略，在20个仿真任务和5个物理机器人任务（含单臂与双臂）中均取得显著性能提升。代码已开源。
原文摘要 · Abstract (English)
Diffusion Policies are effective at learning closed-loop manipulation policies from human demonstrations but generalize poorly to novel arrangements of objects in 3D space, hurting real-world performance. To address this issue, we propose Spherical Diffusion Policy (SDP), an SE(3) equivariant diffusion policy that adapts trajectories according to 3D transformations of the scene. Such equivariance is achieved by embedding the states, actions, and the denoising process in spherical Fourier space. Additionally, we employ novel spherical FiLM layers to condition the action denoising process equivariantly on the scene embeddings. Lastly, we propose a spherical denoising temporal U-net that achieves spatiotemporal equivariance with computational efficiency. In the end, SDP is end-to-end SE(3) equivariant, allowing robust generalization across transformed 3D scenes. SDP demonstrates a large performance improvement over strong baselines in 20 simulation tasks and 5 physical robot tasks including single-arm and bi-manual embodiments. Code is available at https://github.com/amazon-science/Spherical_Diffusion_Policy.
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