Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies
cs.RO
Submitted: 2026-10-05
Updated: 2026-10-05
Sources
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- World Action Models in Real Time: An Empirical Study of Smooth Execution via Asynchronous Deployment
- Training-Time Action Conditioning for Efficient Real-Time Chunking
- VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
- FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking
- SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
- Action-Prior Denoising for Smooth Real-Time Chunking
- Real-Time Robot Execution with Masked Action Chunking
- DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors
- Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models
- Self-Guided Action Diffusion
- ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning
- FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy
- Smoother Action Chunking Flow Policy via Prior-Corrected Orthogonal Trust-Region Guidance
- WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
- Temporal Policy: History-Initialized Action Generation for Robotic Learning from Demonstration
- Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies
- Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
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