PREFAIL：识别机器人提升和放置任务中故障的先兆以提高任务执行性能
PREFAIL: Identifying Precursors to Failures in Robotic Lift-and-Place Tasks to Improve Task Execution Performance
- University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究非prehensile物料搬运的提升和放置任务，提出PREFAIL方法通过分析目标物体与载体相对运动预测故障先兆，引入精确标识最新干预时间的数据集，经实验验证该方法显著提升故障先兆响应的准确性与及时性。
AI中文摘要：
非prehensile操作可通过零件载体实现灵活的物料搬运，但基于摩擦的支撑使高速运动容易出现故障，而较慢的操作会增加周期时间。因此，主动故障预测对于高效可靠的性能至关重要，但现有方法仍受到关键限制，包括对动态动作的敏感性和对已知策略结构的高度依赖。此外，现有方法和数据集缺乏对最新干预时间的精确表征，不清楚检测到的故障是否仍可通过及时干预来预防。在本文中，我们研究了用于非prehensile物料搬运操作的提升和放置任务，并通过分析目标物体相对于载体的相对运动，提出了一种更有效的方法来预测故障先兆（PREFAIL）。我们还引入了一个数据集，该数据集精确识别了危险操作的最新干预时间，从而能够严格评估故障预测是否可行。我们在模拟和真实世界数据集上验证了我们的方法。实验结果表明，PREFAIL大大提高了对故障先兆响应的准确性和及时性。
英文摘要：
Non-prehensile manipulation enables flexible material handling with part carriers, but friction-based support makes high-speed motions failure-prone, while slower operation increases cycle time. Proactive failure prediction is therefore essential for efficient and reliable performance, yet existing approaches remain limited by key constraints, including sensitivity to dynamic actions and high dependence on known policy structures. Furthermore, existing methods and datasets lack a precise characterization of the latest intervention time, leaving it unclear whether a detected failure can still be prevented through timely intervention. In this paper, we investigate lift-and-place tasks for non-prehensile material handling manipulation and propose a more effective approach to predicting precursors to failures (PREFAIL) by analyzing the relative motion of target objects with respect to the carrier. We further introduce a dataset that precisely identifies the latest intervention time for risky manipulations, enabling rigorous evaluation of whether a failure prediction is actionable. We validate our approach on both simulation and real-world datasets. Our experimental results demonstrate that PREFAIL substantially improves both the accuracy and timeliness of responses to failure precursors.
