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Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm. The lower-level network integrates expert experience through Behavior Cloning (BC), giving the robot human-like intuition, and incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance training stability and robustness. The upper-level network dynamically adjusts the lower-level decisions based on heuristic rules, ensuring flexibility in operations. A simulated model is constructed to learn before transferring to real

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