Physical human-robot interaction has been an area of interest for decades. Collaborative tasks, such as joint compliance, demand high-quality joint torque sensing. While external torque sensors are reliable, they come with the drawbacks of being expensive and vulnerable to impacts. To address these issues, studies have been conducted to estimate external torques using only internal signals, such as joint states and current measurements. However, insufficient attention has been given to friction hysteresis approximation, which is crucial for tasks involving extensive dynamic to static state transitions. In this paper, we propose a deep-learning-based method that leverages a novel long-term memory scheme to achieve dynamics identification, accurately approximating the static hysteresis. We also introduce modifications to the well-known Residual Learning architecture, retaining high accuracy while reducing inference time. The robustness of the proposed method is illustrated through a joint compliance and task compliance experiment.

翻译：物理人机交互数十年来一直是研究热点。协作任务（如关节柔顺控制）对高质量的关节力矩感知有较高需求。尽管外部力矩传感器可靠，但其成本高昂且易受冲击。为解决这些问题，已有研究尝试仅利用内部信号（如关节状态和电流测量）来估计外力矩。然而，针对摩擦迟滞近似的研究尚不充分，而这对于涉及频繁动态-静态过渡的任务至关重要。本文提出一种基于深度学习的方法，利用新型长时记忆机制实现动力学辨识，精确近似静态迟滞。我们还对著名的残差学习架构进行了改进，在保持高精度的同时缩短推理时间。通过关节柔顺与任务柔顺实验验证了所提方法的鲁棒性。