GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies

2026-09-17

AI Summary

为了解决固定行动范围无法适应变化控制需求的问题，提出了一种基于几何的自适应行动分块方法GeoAAC，通过分析预测几何特性动态调整行动范围。

Abstract

Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fixed action horizon. During a rollout, different task stages may require different levels of action continuity, control precision, and closed-loop feedback, making a fixed horizon unable to accommodate changing control requirements. We propose \textbf{GeoAAC}, a geometry-based adaptive action chunking method for flow-based VLA policies that adjusts the action horizon according to the reliability of the current action prediction. We show that the geometry of Flow Matching denoising trajectories provides process-level information for characterizing prediction reliability, with geometric variation across action prefixes remaining positively correlated with predictive uncertainty. GeoAAC uses this prefix-wise geometry to construct a horizon-wise geometric profile and adaptively determine the action horizon from a single generation without additional training. Experiments with GR00T N1.5 and π0.5 on LIBERO, LIBERO-Pro, RoboCasa365, and real-world manipulation tasks show consistent improvements over fixed-action-horizon baselines and existing adaptive methods, including up to 8.7 percentage points in simulation and an increase in average real-world success rate from 53.3\% to 74.4\%.

Problem

• Action Chunking
• Vision-Language-Action Policies
• Fixed Action Horizon
• Control Precision
• Adaptive Action

Innovation

• Geometry-based adaptive action chunking
• Flow Matching denoising trajectories
• Action horizon adjustment
• Prediction reliability