用预训练模型提前学习机器人动力学，4秒完成无传感器接触检测校准。
Fast Payload Calibration for Sensorless Contact Estimation Using Model Pre-training
- 预先用神经网络学习全关节空间的动力学，减少在线数据采集
- 仅需4秒轨迹即可完成在线校准，提升动态变化场景下的精度
- 适合频繁重校的工业与协作机器人任务，尤其在负载变动时
力与力矩感知在协作及工业机器人操作中至关重要。传统动力学辨识方法可在无需昂贵传感器的情况下检测和控制外部力与力矩，但在末端执行器负载变化时表现受限。现有校准技术因关节空间覆盖问题，在效率与精度间存在权衡。本文提出一种校准方案：利用预训练神经网络模型预先学习大范围关节空间内的校准动力学。该离线学习策略显著减少在线数据收集需求，无论用于最优模型选择或负载特征识别，仅需4秒轨迹即可完成在线校准。该方法在需要频繁动力学重校以实现精确接触估计的任务中尤为有效。进一步实验验证了其在无传感器关节与任务柔顺性中的应用效果，可应对负载变化带来的影响。
原文摘要 · Abstract (English)
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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