Robotics
ORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI
A new benchmark tackles data contamination to ensure true learning in robotic language models.
Adapting language models to new domains via continual pre-training raises a basic evaluation problem: if the training corpus overlaps with what the model already knows, performance gains cannot be cleanly attributed to new learning rather than pre-existing knowledge. This matters...
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ArXiv CS.RO
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ArXiv CS.RO
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The ORDER benchmark provides a rigorous framework to decouple genuine domain adaptation from existing training data contamination in embodied AI models. This advancement is critical for developing robots that can reliably learn and generalize across diverse, unfamiliar environments without relying on prior memorization.
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