META-Robot: Self-Cognition and Capability Scaling in Agentic Robots
META-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.
The paper introduces META-Robot, a framework combining self-cognition with capability scaling for LLM-based robotic agents, using action-level predicates and proprioceptive feedback to improve task generalization and skill expansion in both simulation and real-world settings.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org