ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
This addresses the problem of slow inference speeds in robotic manipulation for researchers and practitioners, representing an incremental improvement over existing diffusion-based methods.
The paper tackles the runtime inefficiency of diffusion models in 3D robotic manipulation by proposing ManiCM, which uses a consistency model to generate robot actions in one-step inference, accelerating state-of-the-art methods by 10 times while maintaining competitive success rates.
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.