知識誘導型階層ポリシー学習による高精度円筒組立
Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances
0.1mmの厳しい公差を持つ円筒部品の高精度組立を実現するため、階層型強化学習フレームワークを提案。下層は行動クローニングとTD3を統合し、上層はヒューリスティックルールで調整する。シミュレーションで学習後、実世界に転移し、高い成功率と安定性を示した。
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著者: Binbin Lian, Xinyu Liu, Tao Sun
分類: cs.RO
原文アブストラクト
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.
