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Rank-1 LoRA fine-tuning can safely and efficiently adapt simulated locomotion policies to real-world robots, slashing fine-tuning time by nearly half while maintaining safety.
Tactile sensing can be efficiently injected into vision-language-action models via feature-wise linear modulation, boosting robot manipulation performance without the computational overhead of large-scale pretraining.
