SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation
This paper presents SurgiCLAW, a hierarchical skill-based framework for precision surgical manipulation.
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Key points
- 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.
- For brain--machine-interface implantation, a representative high-precision surgical task, we construct a state-to-skill invocation dataset and expand it through visual and state perturbations.
- We evaluate subtask success, offline model comparison, real-robot performance, and ablations.
Sources (1)
- [1]SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic MicromanipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 04:45 PM
This paper presents SurgiCLAW, a hierarchical skill-based framework for precision surgical manipulation.
Recent multimodal large language models (MLLMs) and vision--language--action models have provided robots with new capabilities for long-horizon task understanding and planning.
Extractive summary: sentences quoted from the sources.
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