Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection
cs.RO
Submitted: 2026-06-18
Updated: 2026-10-12
Project page: https://paint-action-chunking.github.io
Terminology
Sources
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Real-Time Execution of Action Chunking Flow Policies
- Training-Time Action Conditioning for Efficient Real-Time Chunking
- VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
- Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling
- Improving and generalizing flow-based generative models with minibatch optimal transport
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- OpenVLA: An Open-Source Vision-Language-Action Model
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models
- Leave No Observation Behind: Real-time Correction for VLA Action Chunks
- SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
- FASTER: Rethinking Real-Time Flow VLAs
- DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors
- Understanding Asynchronous Inference Methods for Vision-Language-Action Models
- You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
- Real-Time Iteration Scheme for Diffusion Policy
- Action-to-Action Flow Matching
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