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%.
- Uses flow-matching trajectory geometry to gauge prediction reliability
- Adapts action horizon per rollout stage without extra training
- Validated on GR00T N1.5 and π0.5 across LIBERO and RoboCasa365
- Real-world manipulation success rises from 53.3% to 74.4%
- Vendors
- NVIDIA
- Products
- GR00T N1.5
- AI models
- GeoAACGR00T N1.5π0.5
From the original article
1 min read at arxiv.orgAction 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.
By Xin Chen · arxiv.org
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