Atomic Action Slicing: Planner-Aligned Options for Generalist VLA Agents

• URL: http://arxiv.org/abs/2512.11584v1
• Date: Fri, 12 Dec 2025 14:14:27 GMT
• Title: Atomic Action Slicing: Planner-Aligned Options for Generalist VLA Agents
• Authors: Stefan Tabakov, Asen Popov, Dimitar Dimitrov, S. Ensiye Kiyamousavi, Vladimir Hristov, Boris Kraychev,
• Abstract summary: Current vision--action models generalize poorly when tasks require new compositions of skills or objects.<n>We introduce Atomic Action Slicing (AAS), a planner-aligned approach that decomposes long-horizon demonstrations into short, typed atomic actions.<n>AAS produces a validated dataset of 2,124 atomic segments labeled with action type, temporal span, and confidence.
• Score: 2.027211672314502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current vision-language-action (VLA) models generalize poorly, particularly when tasks require new compositions of skills or objects. We introduce Atomic Action Slicing (AAS), a planner-aligned approach that decomposes long-horizon demonstrations into short, typed atomic actions that are easier for planners to use and policies to learn. Using LIBERO demonstrations, AAS produces a validated dataset of 2,124 atomic segments labeled with action type, temporal span, and confidence. A stronger segmenter (Gemini 2.5 Pro) closely matches planner-defined plans and remains robust under keyframe jitter, while smaller models perform worse on multi-object tasks. Fine-tuning CLIP-RT+ on our atomic dataset improves task success from 94.2% to 95.3% on LIBERO-Goal and 83.8% to 88.8% on LIBERO-Long. We publicly release the GATE-VLAP dataset on HuggingFace(https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets)

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