GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
GeoAAC adaptively sizes action chunking horizons in flow-based VLA policies using denoising trajectory geometry, raising real-world manipulation success from 53.3% to 74.4%.
GeoAAC exploits geometric variation across action prefixes in flow-matching denoising trajectories as a process-level signal of prediction reliability. It constructs a horizon-wise geometric profile and adaptively determines the action horizon from a single generation without additional training. Experiments with GR00T N1.5 and π0.5 on LIBERO, LIBERO-Pro, and RoboCasa365 show gains up to 8.7 percentage points over fixed-horizon and adaptive baselines, with real-world success rate rising from 53.3% to 74.4%.
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