ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation

Understand

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.