用机器人本体感知实现三类物体接触识别，准确率达91.11%。
Multi-Class Human/Object Detection on Robot Manipulators using Proprioceptive Sensing
- 基于时间序列数据，使用LSTM、GRU和Transformer构建三分类检测模型。
- 在真实测试中达到91.11%准确率，优于以往二分类方法。
- 滑动窗口预处理效果最佳，适合实时应用，适合机器人安全交互研究者。
在物理人机协作（pHRC）场景中，人与机器人在共享环境中直接协同工作。为确保安全并支持有意义的工作流程，机器人需分析与物体的交互，其中关键环节是识别接触物体。以往研究采用二分类机器学习模型区分软硬物体。本研究改进该方法，评估三类人类/物体接触检测模型，实现更细致的接触分析。实验使用Franka Emika Panda机械臂采集数据，探索时序数据分析的预处理策略。训练了LSTM、GRU及Transformer模型。最优模型在实时测试中达到91.11%准确率，验证了多类别检测模型的可行性。此外，对比结果表明滑动窗口预处理策略在此任务中表现最优。
原文摘要 · Abstract (English)
In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments. Robots must analyze interactions with objects to ensure safety and facilitate meaningful workflows. One critical aspect is human/object detection, where the contacted object is identified. Past research introduced binary machine learning classifiers to distinguish between soft and hard objects. This study improves upon those results by evaluating three-class human/object detection models, offering more detailed contact analysis. A dataset was collected using the Franka Emika Panda robot manipulator, exploring preprocessing strategies for time-series analysis. Models including LSTM, GRU, and Transformers were trained on these datasets. The best-performing model achieved 91.11\% accuracy during real-time testing, demonstrating the feasibility of multi-class detection models. Additionally, a comparison of preprocessing strategies suggests a sliding window approach is optimal for this task.
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