META-Robot: Self-Cognition and Capability Scaling in Agentic Robots
Overview
This research presents META-Robot, a framework that integrates self-cognition with capability scaling for LLM-based robotic agents. By using action-level predicates to make execution outcomes explicit, the system enables agents to evaluate their own limitations, extend evaluation criteria, and guide structured skill expansion. Proprioceptive feedback grounds this process in the robot's physical state. Experiments in simulation and on real robots show improved task performance, better generalization to new tasks, continued skill expansion, and effective use of non-visual proprioceptive signals.
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- arXiv Robotics — research abstractsSignalMETA-Robot: Self-Cognition and Capability Scaling in Agentic Robots
This research presents META-Robot, a framework that integrates self-cognition with capability scaling for LLM-based robotic agents. By using action-level predicates to make execution outcomes explicit, the system enables agents to evaluate their own limitations, extend evaluation criteria, and guide structured skill expansion. Proprioceptive feedback grounds this process in the robot's physical state. Experiments in simulation and on real robots show improved task performance, better generalization to new tasks, continued skill expansion, and effective use of non-visual proprioceptive signals.
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