PAIR: Bridging Perception and Action in Vision-Language-Action Models

arXiv cs.AIOctober 8, 2026en

arXiv:2610.09016v1 Announce Type: new Abstract: Vision-language-action (VLA) models map visual observations and language instructions to continuous robot actions. This task requires a transition from representations that describe the scene and instruction to representations that support action generation. Many continuous-action VLAs leave this transition implicit and supervise it mainly through the final action-prediction loss. We introduce PAIR, a framework that learns a shared perception-action representation between these two spaces. During training, a Masked Action Autoencoder encodes expert action chunks into horizon-aligned Action Latent Tokens. A Bridge Module extracts task-relevant f

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• Verktyg
• Forskning
• Robotik