[2511.21428] From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.21428 (cs) [Submitted on 26 Nov 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings Authors:Jiajie Zhang, Sören Schwertfeger, Alexander Kleiner View a PDF of the paper titled From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings, by Jiajie Zhang and 1 other authors View PDF HTML (experimental) Abstract:We present a novel unsupervised framework to unlock vast unlabeled human demonstration data from continuous industrial video streams for Vision-Language-Action (VLA) model pre-training. Our method first trains a lightweight motion tokenizer to encode motion dynamics, then employs an unsupervised action segmenter leveraging a novel "Latent Action Energy" metric to discover and segment semantically coherent action primitives. The pipeline outputs both segmented video clips and their corresponding latent action sequences, providing structured data directly suitable for VLA pre-training. Evaluations on public benchmarks and a proprietary electric motor assembly dataset demonstrate effective segmentation of key tasks performed by humans at workstations. Further clustering and quantitative assessment via a Vision-Language Model confirm the semantic coherence of the discovered action primitives. To our knowledge, this is the first...