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。