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. Their broad availability, high sampling rates, and low dimensionality enable high temporal resolution and efficient processing. 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 tasks, each presenting several anomalies. Test scenarios in robotic cabling, screwing, and sanding are built, and multi-modal time series data is gathered. Several autoencoder-based methods are compared, and we evaluate the generalization across different 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 above 0.96 in failures in the cabling and screwing task, such as incorrect or misaligned parts. In the polishing task, only severe failures were reliably detected, while more subtle failures 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，限制了成功率并增加了损坏风险。异常检测（AD）能够识别数据中偏离预期模式的偏差，可用于触发故障安全行为和恢复策略。先前的研究已将数据驱动的AD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和任务类型间的可迁移性尚未得到验证。利用时序数据（如力/力矩信号）可直接捕获机器人与环境的交互，这对操作和在线故障检测至关重要。其广泛的可用性、高采样率和低维度特性支持高时间分辨率和高效处理。由于机器人任务的信号特征与要求差异显著，需要能以相同方式应用于广泛任务的AD方法，且理想情况下应具备良好的数据效率。我们研究了三种工业任务，每种任务呈现多种异常。构建了机器人布线、拧紧和打磨的测试场景，并采集了多模态时序数据。比较了多种基于自编码器的方法，评估了其在不同任务和控制方法（扩散策略控制、位置控制和阻抗控制）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环境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、检测延迟和任务特性。结果表明，在布线和拧紧任务中（如零件错误或错位）的故障检测可靠，AUROC高于0.96。在打磨任务中，仅能可靠检测严重故障，而更细微的故障则未被检测到。