LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints
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arXiv:2609.01596v1 Announce Type: cross Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pre...
IndustrialVLA-Bench evaluates six released vision‑language‑action (VLA) and world‑action (WAM) robot policy models using a unified reporting schema. It measures clean capability on LIBERO, non‑language robustness on LIBERO‑Plus, instruction sensitivity on LIBERO‑Para, and observed execution cost, aggregating results across three random seeds. The benchmark also reports inference latency, peak memory, runtime mode, and evidence status for each system, enabling traceable comparisons without claiming one paradigm is universally superior.
The paper introduces HiRe-MoMa, a hierarchical residual policy learning framework that fine‑tunes a frozen base policy for mobile manipulation. It uses a policy‑agnostic residual split into mobile‑base and manipulator‑arm heads to reduce gradient interference, and incorporates expert‑guided value alignment to improve sample efficiency with sparse human interventions. Experiments on the TIAGo++ platform show that HiRe‑MoMa boosts task performance after only about 30 minutes of online interaction, regardless of the underlying base policy.
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predi...
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.
arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.