腱駆動連続体ロボットの精密追従制御のための参照拡張学習
Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
腱駆動連続体ロボットの精密な6自由度追従制御のため、微分可能なRNNダイナミクスモデルを勾配ブリッジとして用い、参照分布を拡張したオフライン学習フレームワークを提案。実験で平均位置誤差を50.9%削減した。
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著者: Ziqing Zou, Ke Qiu, Haojian Lu, Rong Xiong, Yue Wang
分類: cs.RO
原文アブストラクト
Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian characteristics. Traditional Jacobian-based controllers often struggle with hysteresis-induced oscillations, while conventional learning-based approaches suffer from poor generalization to out-of-distribution trajectories. This paper proposes a reference-augmented offline learning framework for precise 6-DOF tracking control of TDCRs. By leveraging a differentiable RNN-based dynamics surrogate as a gradient bridge, we optimize a control policy through an augmented reference distribution. This multi-scale augmentation scheme incorporates stochastic bias, harmonic perturbations, and random walks, forcing the policy to internalize diverse tracking error recovery mechanisms without additional hardware interaction. Experimental results on a three-section TDCR platform demonstrate that the proposed policy achieves a 50.9\% reduction in average position error compared to non-augmented baselines and significantly outperforms Jacobian-based methods in both precision and stability across various speeds. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.
