LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
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 ...
| Authors | Baochang Ren, Xinjie Liu, Xi Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 June 2026 |
| Last updated at source | 15 June 2026 |
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