Meta-Control: 異種ロボットスキルに対する自動モデルベース制御合成
Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
LLMを活用し、人間の専門家が制御系を設計する思考過程を模倣して、タスクごとにカスタマイズされた状態表現と制御戦略を自動合成する手法を提案。
著者: Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao, Changliu Liu
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion while others require force compliance; some tasks require avoidance of certain regions, while others require convergence to certain states. Satisfying these varied requirements with a fixed state-action representation and control strategy is challenging, impeding the development of a universal robotic foundation model. In this work, we propose Meta-Control, the first LLM-enabled automatic control synthesis approach that creates customized state representations and control strategies tailored to specific tasks. Our core insight is that a meta-control system can be built to automate the thought process that human experts use to design control systems. Specifically, human experts heavily use a model-based, hierarchical (from abstract to concrete) thought model, then compose various dynamic models and controllers together to form a control system. Meta-Control mimics the thought model and harnesses LLM's extensive control knowledge with Socrates' "art of midwifery" to automate the thought process. Meta-Control stands out for its fully model-based nature, allowing rigorous analysis, generalizability, robustness, efficient parameter tuning, and reliable real-time execution.
