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.

翻译：现实世界中的操作任务需求多样且常常相互冲突：某些任务需要精确运动，而其他任务则需要力柔顺性；部分任务要求避开特定区域，而另一些任务则需收敛至特定状态。使用固定的状态-动作表示与控制策略来满足这些多样化需求具有挑战性，这阻碍了通用机器人基础模型的发展。本研究提出元控制方法，这是首个基于大语言模型的自动控制综合方法，能够针对特定任务创建定制化的状态表示与控制策略。我们的核心洞见在于：可以构建元控制系统来自动化人类专家设计控制系统时所采用的思维过程。具体而言，人类专家大量使用基于模型的、分层（从抽象到具体）的思维模型，进而组合多种动力学模型与控制器以形成控制系统。元控制通过苏格拉底“助产术”的思想，模拟该思维模型并利用大语言模型广泛的控制知识来自动化这一思维过程。元控制的突出优势在于其完全基于模型的特性，支持严格的理论分析，并具备良好的泛化性、鲁棒性、高效参数调优能力以及可靠实时执行性能。