LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints
Summary
LIBERO-Industry introduces a benchmark built on LIBERO that translates industrial process constraints—such as product variants, conveyor speed, execution windows, and geometric tolerances—into 216 controllable robot learning tasks, evaluating five generalist robot policies under realistic high-mix, low-volume production conditions.
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Paper page - LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints
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Abstract
LIBERO-Industry introduces a benchmark translating industrial manufacturing constraints into controllable robot learning tasks to evaluate generalist policies under realistic production conditions.
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 assessinggeneralist robot policies. To address this gap, we introduceLIBERO-Industry, a novel benchmark built on the widely used robot learning benchmark LIBERO, that translates the aforementioned industrial constraints into controllable task conditions and measurable success criteria. Specifically,LIBERO-Industrycomprises three classic task scenarios:Static-to-Dynamic(S2D) loading,Dynamic-to-Static(D2S) unloading, andDynamic-to-Dynamic(D2D) transfer. The suites contain 216 tasks in total that systematically vary object geometry,conveyor speed,interaction-window duration, andgeometric acceptance regionsize. Extensive experiments across fivegeneralist robot policies(includingvision-language-action modelsandworld-action models) show that performance progressively degrades as industrial constraints are introduced and combined. Under a shared fine-tuning and evaluation protocol, theLIBERO-Industryco-training condition achieved higher observed target-transfer completion than the original-LIBERO condition.
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