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.

翻译：机器人操作中的分布外状态常导致不可预测的机器人行为或任务失败，从而降低成功率并增加损坏风险。异常检测（AD）能够识别数据中偏离预期模式的情况，可用于触发故障安全行为和恢复策略。先前研究已将数据驱动的AD方法应用于特定机器人任务的时序数据，但AD方法在不同机器人控制策略与任务类型间的可迁移性尚未得到验证。利用力/力矩信号等时序数据可直接捕捉机器人-环境交互作用，这对操作任务和在线故障检测至关重要。由于机器人任务的信号特征与需求差异显著，需要能够以相同方式应用于广泛任务的AD方法，并具备良好的数据效率。本研究考察了三种工业机器人任务：线缆装配、拧紧操作和打磨抛光，每种任务均包含多模态时序数据及多种异常类型。我们比较了多种基于自编码器的方法，并评估了其在不同机器人任务与控制方法（扩散策略控制、位置控制与阻抗控制）间的泛化能力。这使我们能够验证AD在涉及更严格公差及机器人与其环境双重变化的复杂任务中的集成效果。此外，我们评估了支持稳健检测的数据效率、检测延迟及任务特征。结果表明：在线缆装配与拧紧任务中，针对零件错装、错位及目标遮挡等故障实现了AUROC超过0.96的可靠检测；在抛光任务中，仅能可靠检测严重故障，而更细微的故障类型仍无法被识别。