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Current VLA models fail at zero-shot robotic personalization, highlighting a critical gap in embodied AI's ability to adapt to user preferences.
Quality-aware pretraining strategies, particularly approach-aware episode conditioning, can significantly outperform plain behavioral cloning in VLA tasks, with optimal choices depending on label granularity and model architecture.
Cats are helping AI researchers: a Bayesian-inspired model that treats context as a prior significantly improves intent inference for non-speaking agents and avoids shortcut biases.