基于模型预训练的无传感器接触估计快速负载标定
Fast Payload Calibration for Sensorless Contact Estimation Using Model Pre-training
AI总结：
本文提出利用预训练神经网络进行快速负载标定，仅需4秒在线轨迹即可实现无传感器接触估计，并验证其在关节与任务柔顺控制中的有效性。
摘要：
力和力矩感知在协作与工业场景下的机器人操作中至关重要。传统的动力学辨识方法无需昂贵传感器即可检测和控制外部力与力矩。然而，当机器人动力学特性（尤其是末端执行器负载）发生变化时，这些方法表现出局限性。此外，由于对关节空间覆盖的考虑，现有标定技术在效率与精度之间存在权衡。本文提出一种标定方案，利用预训练的神经网络模型提前学习大范围关节空间中的标定动力学。这种离线学习策略显著减少了对在线数据采集的需求，无论是用于选择最优模型还是辨识负载特征，在线标定仅需一段4秒的轨迹。该方法在需要频繁重新标定动力学以实现精确接触估计的任务中尤为有效。我们进一步通过无传感器关节柔顺和任务柔顺的应用，验证了该方法在考虑负载变化情况下的有效性。
英文摘要：
Force and torque sensing is crucial in robotic manipulation across both collaborative and industrial settings. Traditional methods for dynamics identification enable the detection and control of external forces and torques without the need for costly sensors. However, these approaches show limitations in scenarios where robot dynamics, particularly the end-effector payload, are subject to changes. Moreover, existing calibration techniques face trade-offs between efficiency and accuracy due to concerns over joint space coverage. In this paper, we introduce a calibration scheme that leverages pre-trained Neural Network models to learn calibrated dynamics across a wide range of joint space in advance. This offline learning strategy significantly reduces the need for online data collection, whether for selection of the optimal model or identification of payload features, necessitating merely a 4-second trajectory for online calibration. This method is particularly effective in tasks that require frequent dynamics recalibration for precise contact estimation. We further demonstrate the efficacy of this approach through applications in sensorless joint and task compliance, accounting for payload variability.
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