ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
Zifeng Gao, Guanxing Lu, Tianxing Chen, Wenxun Dai, Ziwei Wang, Chao Shang, Wenbo Ding, Yansong Tang
cs.RO, cs.AI
Submitted: 2026-08-18
Updated: 2026-08-19
Comments: https://manicm-fast.github.io/
Project page: https://manicm-fast.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- RT-1: Robotics Transformer for Real-World Control at Scale
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
- AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
- DNAct: Diffusion Guided Multi-Task 3D Policy Learning
- ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
- What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
- Denoising Diffusion Implicit Models
- DiffuserLite: Towards Real-time Diffusion Planning
- Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
- 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
- LCM-LoRA: A Universal Stable-Diffusion Acceleration Module
- PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models
- CCM: Adding Conditional Controls to Text-to-Image Consistency Models
- VideoLCM: Video Latent Consistency Model
- AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
- MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
- ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
- Boosting Continuous Control with Consistency Policy
- Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
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