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IndustrialVLA-Bench is presented, an evidence-aware evaluation of six released VLA and WAM systems under a unified reporting schema that evaluates clean capability on LIBERO, non-language robustness on LIBERO-Plus, instruction sensitivity on LIBERO-Para, and observed execution cost.
Humanoid-OmniOcc reveals that a stereo-based dataset can dramatically enhance occupancy prediction accuracy for humanoid robots, outperforming traditional monocular methods.
Achieve significant cost savings in LLM reinforcement learning by overlapping rollout generation, dissemination, and training with a framework that tolerates bounded policy staleness.