ロボット組立・ねじ締め・マニピュレーションにおける汎用的な失敗監視のための異常検知
Anomaly detection for generic failure monitoring in robotic assembly, screwing and manipulation
力覚などの時系列データに自己符号化器ベースの異常検知を適用し、配線・ねじ締め・研磨の3つの産業タスクで、制御手法やタスクをまたいだ汎化性能とデータ効率を評価した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Niklas Grambow, Lisa-Marie Fenner, Felipe Kempkes, Philip Hotz, Dingyuan Wan, Jörg Krüger, Kevin Haninger
分類: cs.RO
原文アブストラクト
Out-of-distribution states in robot manipulation often lead to unpredictable robot behavior or task failure, limiting success rates and increasing risk of damage. Anomaly detection (AD) can identify deviations from expected patterns in data, which can be used to trigger failsafe behaviors and recovery strategies. Prior work has applied data-driven AD on time series data for specific robotic tasks, however the transferability of an AD approach between different robot control strategies and task types has not been shown. Leveraging time series data, such as force/torque signals, allows to directly capture robot-environment interactions, crucial for manipulation and online failure detection. As robotic tasks can have widely signal characteristics and requirements, AD methods which can be applied in the same way to a wide range of tasks is needed, ideally with good data efficiency. We examine three industrial robotic tasks, robotic cabling, screwing, and sanding, each with multi-modal time series data and several anomalies. Several autoencoderbased methods are compared, and we evaluate the generalization across different robotic tasks and control methods (diffusion policy-, position-, and impedance-controlled). This allows us to validate the integration of AD in complex tasks involving tighter tolerances and variation from both the robot and its environment. Additionally, we evaluate data efficiency, detection latency, and task characteristics which support robust detection. The results indicate reliable detection with AUROC exceeding 0.96 in failures in the cabling and screwing task, such as incorrect or misaligned parts and obstructed targets. In the polishing task, only severe failures were reliably detected, while more subtle failure types remained undetected.
