Achieves high-fidelity one-step (1 NFE) 3D robotic manipulation using training-time drifting fields.
March 13, 2026
Original Paper
Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation
arXiv · 2603.11984
The Takeaway
Diffusion policies are slow; existing one-step methods collapse action modes. Ada3Drift moves the iterative refinement from inference to training, allowing robots to perform complex 3D tasks 10x faster than diffusion-based alternatives while maintaining multimodal behavior.
From the abstract
Diffusion-based visuomotor policies effectively capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control. Recent flow matching and consistency-based methods achieve single-step generation, yet sacrifice the ability to preserve distinct action modes, collapsing multimodal behaviors into averaged, often physically infeasible trajectories. We observe that the compute budget asymmetry in robotics (offline training vs.\ rea