  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.
