Teleoperation of low-cost manipulators is attracting increasing attention as a practical means of collecting demonstration data for imitation learning. However, most existing low-cost systems rely on unilateral position control without force feedback, while implementing force-feedback bilateral teleoperation is difficult because low-cost manipulators typically have low-resolution encoders and no joint torque sensors. This paper presents a sensorless 4-channel bilateral teleoperation framework that integrates identified nonlinear dynamics compensation with a disturbance-observer-based velocity and external-force estimation scheme. By interpreting the observer structure in the frequency domain, we clarify the coupling between the velocity- and external-force-estimation bandwidths and derive practical tuning guidelines based on the damping ratio and a single cutoff frequency. Real-robot experiments, including force-sensor comparison and teleoperation tasks, demonstrate that the proposed framework provides practically useful force estimates and enables stable teleoperation in high-speed and contact-rich scenarios under low-cost hardware constraints. As an application, imitation-learning experiments demonstrate that incorporating estimated force information into demonstrations improves task success rates in the tested contact-rich manipulation tasks.

翻译：低成本机械臂的遥操作作为收集模仿学习示范数据的一种实用手段正日益受到关注。然而，现有大多数低成本系统依赖无力反馈的单向位置控制，而实现力反馈双边遥操作十分困难，这是因为低成本机械臂通常配备低分辨率编码器且无关节力矩传感器。本文提出了一种无传感四通道双边遥操作框架，该框架将辨识的非线性动力学补偿与基于扰动观测器的速度和外力估计方案相结合。通过在频域中解释观测器结构，阐明了速度估计带宽与外力估计带宽之间的耦合关系，并基于阻尼比和单一截止频率推导了实用的调优准则。真实机器人实验（包括力传感器对比和遥操作任务）表明，所提框架能够提供实际可用的力估计，并在低成本硬件约束下实现高速及密集接触场景中的稳定遥操作。作为应用，模仿学习实验证明，在测试的密集接触操作任务中，将估计的力信息融入示范可提升任务成功率。