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

Published September 18, 2026 · Category: Robotics

Overview

arXiv:2609.20776v1 Announce Type: new 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 {\pi}0.5 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\%.

Source

Originally published at arxiv.org.

Related Articles

• GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
• Time-Efficient Iterative Learning Planning for Safety-Critical Dynamic Obstacle Avoidance
• MoWAM: Explicit Future Motion Prediction for Efficient World Action Models