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• ManipArena provides a standardized evaluation framework that bridges simulation and real-world execution for reasoning-oriented robot manipulation.method 95%
• Existing simulator-centric benchmarks fail to capture the reality gap caused by perception noise, complex contact dynamics, hardware constraints, and system latency.limitation 90%
• ManipArena's stratified OOD evaluation design enables disentangling the individual and combined effects of out-of-distribution factors on model performance.method 85%
• The DreamZero world model requires 50–70× more time per action step than VLA models like π0.5, making it significantly slower for real-time control.result 90%
• Multi-task training enhances cross-task visual recognition but impairs task-specific procedural memory for instruction-conditioned sequential tasks.result 85%
• ManipArena includes 20 diverse tasks across 10,812 expert trajectories with emphasis on reasoning-oriented manipulation requiring semantic and spatial reasoning.method 90%
• The benchmark's green-screen environment eliminates uncontrolled visual confounders, ensuring performance differences are attributable to designed variables.method 80%

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