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Recent multimodal large language models (MLLMs) and vision--language--action models have provided robots with new capabilities for long-horizon task understanding and planning. In surgical robotics, however, end-to-end prediction of low-level actions remains limited by insufficient precision, opaque decisions, and scar...
The proposed MedVLA is a hierarchical framework that couples high-level multimodal reasoning with lowlevel function-constrained execution for deployable precision medical robotics, and demonstrates the effectiveness of the proposed framework across model variants.
HALO-WA is proposed, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors.
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