ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence

arXiv:2609.39754 · cs.RO · Submitted 2026-09-30 · Read on arXiv

cs.RO

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Action Chunking — 13× in this paper · explained in 1 episode
• Adaptation — 11× in this paper · explained in 1 episode
• Trace — 11× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Adaptive Action Chunking — 4× in this paper
• Flow Matching — 4× in this paper · explained in 8 episodes
• Query-based Horizon Adapter — 4× in this paper
• Verification — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Beijing Academy of Artificial Intelligence — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Non-Markovianity — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Motus: A Unified Latent Action World Model
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Dynamic Execution Commitment of Vision-Language-Action Models
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• DREAM-Chunk: Reactive Action Chunking with Latent World Model
• GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
• StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
• Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
• Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models
• ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
• FASTER: Rethinking Real-Time Flow VLAs
• PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
• Spatial Attention: Adapting Execution Horizons for Diffusion Policies via Observation Sensitivity
• Taming Non-stationary Bandits: A Bayesian Approach
• Leave No Observation Behind: Real-time Correction for VLA Action Chunks
• RoboBrain 2.0 Technical Report

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving