  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.
