输入门控双边遥操作：一种易于在低成本硬件上实现的力反馈遥操作方法
Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
- Research & Development Group, Hitachi, Ltd.（日立株式会社研发部）
- Department of Intermedia Art and Science School of Fundamental Science and Engineering, Waseda University（早稻田大学基础科学与工程学部多媒体艺术与科学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对接触丰富操作中力反馈遥操作复杂难实现的问题，提出一种仅需简单反馈控制器、无需力传感器的输入门控双边遥操作方法，在低成本硬件上实现高可操作性与接触稳定性，并具有高鲁棒性。
AI中文摘要：
在接触丰富的操作中，有效的数据收集需要在遥操作过程中提供力反馈，因为对接触的准确感知对于稳定控制至关重要。然而，此类技术仍不常见，主要原因是双边遥操作系统复杂且难以实现。为克服这一难题，我们提出了一种仅依赖简单反馈控制器且无需力传感器的双边遥操作方法。该方法专为使用低成本硬件的领导-跟随（leader-follower）配置而设计，具有广泛的适用性。通过数值仿真和真实世界实验，我们证明了该方法只需最少的参数调整，即可同时实现高可操作性和接触稳定性，性能优于传统方法。此外，我们展示了其高鲁棒性：即使在领导端与跟随端之间通信周期率较低的情况下，与高速运行相比，控制性能的退化也极小。我们还证明，该方法可在两种市售低成本硬件上实现，且无需任何参数调整。这凸显了其易于实现和高通用性的特点。我们期望该方法将扩大力反馈遥操作系统在低成本硬件上的应用，从而推动模仿学习中接触丰富任务自主性的发展。
英文摘要：
Effective data collection in contact-rich manipulation requires force feedback during teleoperation, as accurate perception of contact is crucial for stable control. However, such technology remains uncommon, largely because bilateral teleoperation systems are complex and difficult to implement. To overcome this, we propose a bilateral teleoperation method that relies only on a simple feedback controller and does not require force sensors. The approach is designed for leader-follower setups using low-cost hardware, making it broadly applicable. Through numerical simulations and real-world experiments, we demonstrate that the method requires minimal parameter tuning, yet achieves both high operability and contact stability, outperforming conventional approaches. Furthermore, we show its high robustness: even at low communication cycle rates between leader and follower, control performance degradation is minimal compared to high-speed operation. We also prove our method can be implemented on two types of commercially available low-cost hardware with zero parameter adjustments. This highlights its high ease of implementation and versatility. We expect this method will expand the use of force feedback teleoperation systems on low-cost hardware. This will contribute to advancing contact-rich task autonomy in imitation learning.
