Summary
OpenCastor, a runtime layer for robot AI agents, revealed that the order of skill pipelines and parameters like thinking_budget significantly affect task success, comparable to the choice of the AI model itself. To address this, a distributed evaluator was built, allowing robots to contribute idle compute to benchmark various harness configurations against a real-world benchmark. This initiative aims to optimize and improve the reliability of robotic AI systems.
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