不確実なロボットシステムにおけるスケーラブルなデータ駆動型安全フィルタのための制約誘導オンラインデータ選択
Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems
モデルベースの証明関数とガウス過程回帰を組み合わせ、オンラインデータ選択で計算量をデータセットサイズに対して二次から線形に削減するデータ駆動型安全フィルタを提案し、実機カートポールと5リンク二足歩行ロボットのシミュレーションで有効性を示した。
著者: Jason J. Choi, Fernando Castañeda, Wonsuhk Jung, Bike Zhang, Claire J. Tomlin, Koushil Sreenath
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
As the use of autonomous robots expands in tasks that are complex and challenging to model, the demand for robust data-driven control methods that can certify safety and stability in uncertain conditions is increasing. However, the practical implementation of these methods often faces scalability issues due to the growing amount of data points with system complexity, and a significant reliance on high-quality training data. In response to these challenges, this study presents a scalable data-driven controller that efficiently identifies and infers from the most informative data points for implementing data-driven safety filters. Our approach is grounded in the integration of a model-based certificate function-based method and Gaussian Process (GP) regression, reinforced by a novel online data selection algorithm that reduces time complexity from quadratic to linear relative to dataset size. Empirical evidence, gathered from successful real-world cart-pole swing-up experiments and simulated locomotion of a five-link bipedal robot, demonstrates the efficacy of our approach. Our findings reveal that our efficient online data selection algorithm, which strategically selects key data points, enhances the practicality and efficiency of data-driven certifying filters in complex robotic systems, significantly mitigating scalability concerns inherent in nonparametric learning-based control methods.
