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ProtoAct, a structured protocol-grounding framework that converts free-form biological procedures into state-aware, embodiment-ready action sequences, is presented, providing a practical interface between biological protocol understanding and embodied robotic execution.
A parameter-efficient large model adaptation framework that integrates retrieval-augmented semantic conditioning modeling with distribution-imbalance-aware contrastive representation learning for agricultural pest and disease text entity recognition is proposed, offering an efficient and scalable pathway for adapting l...
This work introduces BioVLN, a simulation platform for developing and evaluating visual-language navigation agents in biomedical laboratories and shows that geometric exploration reaches 74.4--87.5% success, while sampling multiple valid positions in the operation area improves success and reduces unsafe proximity.
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