Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
- URL: http://arxiv.org/abs/2405.11380v2
- Date: Fri, 7 Jun 2024 15:22:41 GMT
- Title: Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
- Authors: Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao, Changliu Liu,
- Abstract summary: We propose Meta-Control, which 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.
- Score: 10.43221469116584
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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