Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
<|reference_start|>Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills: 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.<|reference_end|>
@article{wei2024meta-control:,
    title={Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous
  Robot Skills},
    author={Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao, Changliu Liu},
    journal={arXiv preprint arXiv:2405.11380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11380},
    primaryClass={cs.RO cs.AI cs.SY eess.SY}
}