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

RESUMO

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 world. An efficient and safe training is allowed. Comparisons show that the reward curve converges within 500 episodes, indicating high learning efficiency. It also demonstrates better adaptability to initial conditions and pose errors, achieving satisfactory success rates even under extreme conditions. Moreover, the method exhibits good stability under Gaussian noise interference. In the real world, the assembly trajectory of the cylindrical segment shows smoother motion and less fluctuation.