ET-SEED：高效的轨迹级 SE(3) 等变扩散策略
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy
AI总结：
为减少机器人模仿学习对大量演示的依赖，本文提出高效的轨迹级 SE(3) 等变扩散模型 ET-SEED，通过扩展等变马尔可夫核简化扩散约束，显著提升端到端训练效率，并在少量演示下实现高数据效率与强泛化能力。
摘要：
模仿学习（如扩散策略）在多种机器人操作任务中被证明有效。然而，策略的鲁棒性和泛化能力需要大量演示。为减少对演示的依赖，我们利用空间对称性，提出了 ET-SEED，一种高效的轨迹级 SE(3) 等变扩散模型，用于在复杂机器人操作任务中生成动作序列。此外，以往的等变扩散模型要求马尔可夫过程中满足逐步等变，使得在如此强约束下难以学习策略。我们在理论上扩展了等变马尔可夫核，并简化了等变扩散过程的条件，从而以端到端方式显著提升了轨迹级 SE(3) 等变扩散策略的训练效率。我们在涉及刚体、铰接体和可变形物体的代表性机器人操作任务上评估了 ET-SEED。实验表明，我们提出的方法具有卓越的数据效率和操作熟练度，并且只需少量演示即可泛化到未见过的配置。
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
