Imitation learning, e.g., diffusion policy, has been proven effective in various robotic manipulation tasks. However, extensive demonstrations are required for policy robustness and generalization. To reduce the demonstration reliance, we leverage spatial symmetry and propose ET-SEED, an efficient trajectory-level SE(3) equivariant diffusion model for generating action sequences in complex robot manipulation tasks. Further, previous equivariant diffusion models require the per-step equivariance in the Markov process, making it difficult to learn policy under such strong constraints. We theoretically extend equivariant Markov kernels and simplify the condition of equivariant diffusion process, thereby significantly improving training efficiency for trajectory-level SE(3) equivariant diffusion policy in an end-to-end manner. We evaluate ET-SEED on representative robotic manipulation tasks, involving rigid body, articulated and deformable object. Experiments demonstrate superior data efficiency and manipulation proficiency of our proposed method, as well as its ability to generalize to unseen configurations with only a few demonstrations. Website: https://et-seed.github.io/

本研究解决了模仿学习中对广泛示例依赖的问题，提出ET-SEED这一高效轨迹级SE(3)等变扩散模型，以生成复杂机器人操作任务中的动作序列。通过理论扩展等变马尔可夫核并简化等变扩散过程的条件，显著提高了策略的训练效率，并在多种机器人操作任务中表现出优越的数据效率和操作能力。 

ET-SEED：高效的轨迹级SE(3)等变扩散策略