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：现实世界操作任务的需求多样且常常相互冲突；某些任务需要精确运动，而其他任务需要力顺应；某些任务需要避开特定区域，而其他任务需要收敛到特定状态。用固定的状态-动作表示和控制策略满足这些多样化的需求颇具挑战性，阻碍了通用机器人基础模型的发展。本文提出Meta-Control，这是首个由大语言模型驱动的自动化控制综合方法，可为特定任务创建定制化的状态表示和控制策略。我们的核心见解在于，可以构建一个元控制系统来自动化人类专家设计控制系统时的思维过程。具体而言，人类专家广泛采用基于模型的层次化（从抽象到具体）思维模型，然后将各种动态模型和控制器组合成控制系统。Meta-Control模仿这一思维模型，并借助大语言模型广泛的控制知识，结合苏格拉底的“助产术”来自动化思维过程。Meta-Control以其完全基于模型的特性脱颖而出，支持严格分析、泛化性、鲁棒性、高效参数调优以及可靠的实时执行。