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

GeoAAC introduces a geometry-based adaptive action chunking technique for Vision‑Language‑Action policies, dynamically adjusting the action horizon based on the reliability of current action predictions. By leveraging the geometric variation in Flow Matching denoising trajectories, GeoAAC constructs a horizon‑wise geometric profile that determines the action horizon during a single generation without extra training. Experiments on LIBERO, LIBERO‑Pro, RoboCasa365, and real‑world manipulation tasks demonstrate consistent gains over fixed‑horizon baselines, achieving up to 8.7 percentage points improvement in simulation and raising real‑world success rates from 53.3% to 74.4%.

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• rag
• diffusion
• robotics
• multimodal