LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints

Industrial manipulation in flexible manufacturing, e.g., high-mix, low-volume (HMLV) electronics assembly, requires robot policies to adapt to changing products and production conditions. Such settings impose industrial process constraints, including product variants, moving material flows, finite execution windows, and tight geometric tolerances. However, existing evaluation frameworks do not systematically capture these constraints when assessing generalist robot policies. To address this gap, we introduce LIBERO-Industry, a benchmark built on the widely used robot learning benchmark LIBERO that translates industrial process constraints into controllable task conditions and measurable success criteria. LIBERO-Industry comprises three industrial workflow scenarios: Static-to-Dynamic (S2D) loading, Dynamic-to-Static (D2S) unloading, and Dynamic-to-Dynamic (D2D) transfer. The benchmark contains 216 tasks that systematically vary object geometry, conveyor speed, interaction-window duration, and geometric acceptance-region size. Experiments across five generalist robot policies, including vision-language-action and world-action models, reveal progressive performance degradation as industrial constraints are introduced and combined; benchmark code and datasets will be made publicly available to support reproducible evaluation. Real-world D2S experiments further show higher physical task success when sim-real co-training uses LIBERO-Industry rather than standard LIBERO simulation data under matched training conditions. Code and data will be made publicly available.