Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
arxiv(2024)
摘要
The requirements for real-world manipulation tasks are diverse and often
conflicting; some tasks necessitate force constraints or collision avoidance,
while others demand high-frequency feedback. 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. Meta-Control leverages a generic
hierarchical control framework to address a wide range of heterogeneous tasks.
Our core insight is the decomposition of the state space into an abstract task
space and a concrete tracking space. By harnessing LLM's extensive common sense
and control knowledge, we enable the LLM to design these spaces, including
states, dynamic models, and controllers, using pre-defined but abstract
templates. Meta-Control stands out for its fully model-based nature, allowing
for rigorous analysis, efficient parameter tuning, and reliable execution. It
not only utilizes decades of control expertise encapsulated within LLMs to
facilitate heterogeneous control but also ensures formal guarantees such as
safety and stability. Our method is validated both in real-world scenarios and
simulations across diverse tasks with conflicting requirements, such as
collision avoidance versus convergence and compliance versus high precision.
Videos and additional results are at meta-control-paper.github.io
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