LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Summary
LabVLA is a vision-language-action model for scientific laboratory automation, trained with a two-stage approach combining action token pretraining and flow matching. It achieves state-of-the-art success rates on the LabUtopia benchmark by leveraging simulated data to bridge the gap between household demonstrations and lab-specific tasks.
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Paper page - LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Source: https://huggingface.co/papers/2606.13578 Authors:
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Abstract
LabVLA, a vision-language-action model trained with a two-stage approach combining action token pretraining and flow matching, demonstrates superior performance on laboratory automation tasks through simulated data generation and robot-specific learning.
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision-Language-Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, asimulation-based workflowanddata enginethat composes configuredlaboratory workflowsfrom atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we presentLabVLA, trained with atwo-stage recipe:FAST action token pretrainingfirst makes theQwen3-VL-4B-Instructbackbone action aware before any continuous control is learned, andflow matchingposttraining then attaches aDiT action expertunderknowledge insulation. On theLabUtopia benchmark,LabVLAachieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.
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