ケーブル駆動ロボットの部分観測可能な動力学をモデル化するグラフニューラルネットワーク
CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics
ケーブル駆動ロボットをグラフとして表現し、部分観測入力のみから動力学を学習するGNNシミュレータを提案。実機とシミュレータの共同学習で汎化性を高め、MPPI制御による閉ループナビゲーションで有効性を示した。
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著者: Nelson Chen, William R. Johnson, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya
分類: cs.RO
原文アブストラクト
General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents \texttt{CableRobotGraphSim}, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model's speed and accuracy.
