SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation
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 scarce data. This paper presents SurgiCLAW, a hierarchical skill-based framework for precision surgical manipulation. SurgiCLAW confines an MLLM to intraoperative visual understanding, structured explanation, and atomic surgical-skill selection, while delegating physical execution to verifiable deterministic control programs. For brain--machine-interface implantation, a represen...
| Authors | Angen Ye, junjie xie, Yujia Song et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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