META-Robot：自己認知と能力拡張を両立するエージェント型ロボット
META-Robot: Self-Cognition and Capability Scaling in Agentic Robots
行動レベルの述語で実行結果を明示化し、LLMが自己の限界を認識して評価基準とスキルを段階的に拡張するフレームワークを提案。固有感覚フィードバックも活用し、実機・シミュレーションで汎化と継続的スキル拡張を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Xuanzhi Feng, Zeyu Liu, Weiheng Gao, Xinchen Li, Jiankun Tang, Fangqi Zhu, Quanxin Shou, Hao Chen, Zhengyang Yan, Hualei Zhang, Junhao Li, Zhiyao Wu, Jie Zhang, Song Guo
分類: cs.RO
原文アブストラクト
LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill repertoires alone offers limited guidance on how to assess new capabilities or identify what remains beyond their reach. We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. At its core, action-level predicates make execution outcomes explicit and available for LLM reasoning. The agent uses these assessments to identify its own limitations, extend relevant evaluation criteria, and guide the structured expansion of its skills. Proprioceptive feedback further grounds this process in the robot's physical state, enriching the evidence available for self-cognition. Experiments in simulation and on real robots demonstrate stronger overall task performance, generalization to new tasks, continued expansion of skills and evaluation criteria, and effective use of non-visual proprioceptive signals.