When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models
cs.RO
Submitted: 2026-10-05
Updated: 2026-10-05
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
- Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing
- Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
- Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
- PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- SAGE:State-Aware Guided End-to-End Policy for Multi-Stage Sequential Tasks via Hidden Markov Decision Process
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