Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills

TL;DR

Meta-Control leverages large language models to automate the synthesis of customized, hierarchical control strategies for heterogeneous robotic tasks, mimicking expert thought processes for improved adaptability and robustness.

Contribution

This work introduces Meta-Control, the first LLM-enabled system that automates control system design by mimicking human expert hierarchical reasoning.

Findings

Automates control synthesis tailored to specific tasks.

Enables rigorous analysis and robustness.

Facilitates real-time execution and parameter tuning.

Abstract

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.