SurgiCLAW: 精密ロボット微細操作のための解釈可能な原子スキルエージェント
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation
MLLMを術中理解とスキル選択に限定し、実行は決定論的制御に委ねる階層型フレームワークを提案。脳BMI電極挿入タスクでスキル選択精度94.6%、実機成功率88%を達成。
著者: Angen Ye, junjie xie, Yujia Song, Junkai Huang, Dapeng Zhang
分類: cs.RO, cs.AI
原文アブストラクト
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 representative high-precision surgical task, we construct a state-to-skill invocation dataset and expand it through visual and state perturbations. The model first learns structured skill invocation through supervised fine-tuning and is then optimized using three rewards for action correctness, operation prerequisites, and format validity, improving action selection, operational safety, and output parseability. We evaluate subtask success, offline model comparison, real-robot performance, and ablations. On a fixed test set, SurgiCLAW achieves 94.6% skill-selection accuracy and 93.0% macro-averaged F1 (Macro-F1). Across 50 closed-loop real-robot implantation trials, it achieves an 88.0% task success rate.