让机器人通过学习操作员动作，自动完成核电废料处理任务。
Teaching Robots to Handle Nuclear Waste: A Teleoperation-Based Learning Approach<
- 用远程操作数据训练模型，让机器人复现人类操作技巧。
- 在插电任务中提升效率，减少对人工持续操控的依赖。
- 适合需要高精度重复操作的工业场景，如核废料处理。
本文提出一种基于远程操作的学习（Learning from Teleoperation, LfT）框架，将人类经验与机器人精准性结合，使机器人能够自主执行从人类操作员处学习到的操作技能。该框架针对核电废料处理中常见的重复性、高精度操作挑战，通过捕捉远程操作过程中的运动轨迹与操作力信息，利用这些数据训练机器学习模型，实现对人类技能的复制与泛化。我们以电源插头插入任务为典型场景进行验证，该任务具有重复性强且需精确轨迹与力控的特点。实验结果表明，该框架显著提升了任务执行效率，同时大幅降低了对操作员持续干预的需求。
原文摘要 · Abstract (English)
This paper presents a Learning from Teleoperation (LfT) framework that integrates human expertise with robotic precision to enable robots to autonomously perform skills learned from human operators. The proposed framework addresses challenges in nuclear waste handling tasks, which often involve repetitive and meticulous manipulation operations. By capturing operator movements and manipulation forces during teleoperation, the framework utilizes this data to train machine learning models capable of replicating and generalizing human skills. We validate the effectiveness of the LfT framework through its application to a power plug insertion task, selected as a representative scenario that is repetitive yet requires precise trajectory and force control. Experimental results highlight significant improvements in task efficiency, while reducing reliance on continuous operator involvement.
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