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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.
Shape-Aware Reinforcement Learned Model Predictive Control is proposed, a method for safe, efficient, and adaptive navigation in crowds with heterogeneous shapes without geometry simplification that preserves the safety structure and generalizability of MPC while integrating the adaptability and intelligence of RL.
ACE-Brain-0.5 is presented, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement, and SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector...
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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