用力觉反馈+预训练模型，让机器人学会自适应擦抹新物体
Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations
- 结合力矩反馈与预训练物体表征，实时调整擦抹力度
- 真实场景下力控精度达96%，远超无反馈方法的4%
- 可应对不同海绵和表面高度变化，适合复杂操作任务
模仿学习为机器人执行重复性任务提供了路径，使人类能专注于更具意义的工作。然而，该方法面临需要大量示范以及训练与真实环境差异的问题。本文聚焦于如使用软质可变形物体进行擦抹等接触密集型任务，需自适应力控以应对表面高度和海绵物理特性变化。为此，我们提出一种新方法，将实时力-扭矩（FT）反馈与预训练物体表征相结合，使机器人能够动态适应此前未见的表面高度和海绵物理属性变化。在真实实验中，该方法在施加参考力方面达到96%的准确率，显著优于缺乏力矩反馈的旧方法（仅4%准确率）。为评估适应性，我们在与训练设置不同的条件下进行了40种情景测试，涵盖10种不同物理特性的海绵和4类擦拭表面高度，通过分析力轨迹验证了机器人适应能力的显著提升。
原文摘要 · Abstract (English)
Imitation learning offers a pathway for robots to perform repetitive tasks, allowing humans to focus on more engaging and meaningful activities. However, challenges arise from the need for extensive demonstrations and the disparity between training and real-world environments. This paper focuses on contact-rich tasks like wiping with soft and deformable objects, requiring adaptive force control to handle variations in wiping surface height and the sponge's physical properties. To address these challenges, we propose a novel method that integrates real-time force-torque (FT) feedback with pre-trained object representations. This approach allows robots to dynamically adjust to previously unseen changes in surface heights and sponges' physical properties. In real-world experiments, our method achieved 96% accuracy in applying reference forces, significantly outperforming the previous method that lacked an FT feedback loop, which only achieved 4% accuracy. To evaluate the adaptability of our approach, we conducted experiments under different conditions from the training setup, involving 40 scenarios using 10 sponges with varying physical properties and 4 types of wiping surface heights, demonstrating significant improvements in the robot's adaptability by analyzing force trajectories. The video of our work is available at: https://sites.google.com/view/adaptive-wiping
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