Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based methods show impressive performance in 3D robotic manipulation tasks, whereas they suffer from severe runtime inefficiency due to multiple denoising steps, especially with high-dimensional observations. To this end, we propose a real-time robotic manipulation model named ManiCM that imposes the consistency constraint to the diffusion process, so that the model can generate robot actions in only one-step inference. Specifically, we formulate a consistent diffusion process in the robot action space conditioned on the point cloud input, where the original action is required to be directly denoised from any point along the ODE trajectory. To model this process, we design a consistency distillation technique to predict the action sample directly instead of predicting the noise within the vision community for fast convergence in the low-dimensional action manifold. We evaluate ManiCM on 31 robotic manipulation tasks from Adroit and Metaworld, and the results demonstrate that our approach accelerates the state-of-the-art method by 10 times in average inference speed while maintaining competitive average success rate.

我们提出了一种名为ManiCM的实时机器人操作模型，该模型通过在扩散过程中施加一致性约束来生成机器人动作，以实现仅进行一步推理。通过在机器人动作空间中制定一致的扩散过程和点云输入条件下对原始动作进行直接降噪操作，我们设计了一种一致性蒸馏技术来直接预测动作样本，而不是预测视觉领域中的噪声，以在低维动作流形中实现快速收敛。我们在Adroit和Metaworld的31个机器人操作任务上评估了ManiCM的性能，结果表明我们的方法平均推理速度提高了10倍，同时保持具有竞争力的平均成功率。

ManiCM: 实时三维扩散策略通过一致性模型用于机器人操纵