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Touch is not just an add-on; it fundamentally enhances object representation, leading to dramatic improvements in physical property estimation and manipulation tasks.
Language components in VLA models are often redundant, allowing for significant performance gains by reducing their size without sacrificing control quality.
Foundation models can be made intrinsically resistant to unauthorized fine-tuning by concentrating learning in a sparsely masked subnetwork that remains private.
