  We report on the development of an implementable physics-data hybrid dynamic
model for an articulated manipulator to plan and operate in various scenarios.
Meanwhile, the physics-based and data-driven dynamic models are studied in this
research to select the best model for planning. The physics-based model is
constructed using the Lagrangian method, and the loss terms include inertia
loss, viscous loss, and friction loss. As for the data-driven model, three
methods are explored, including DNN, LSTM, and XGBoost. Our modeling results
demonstrate that, after comprehensive hyperparameter optimization, the XGBoost
architecture outperforms DNN and LSTM in accurately representing manipulator
dynamics. The hybrid model with physics-based and data-driven terms has the
best performance among all models based on the RMSE criteria, and it only needs
about 24k of training data. In addition, we developed a virtual force sensor of
a manipulator using the observed external torque derived from the dynamic model
and designed a motion planner through the physics-data hybrid dynamic model.
The external torque contributes to forces and torque on the end effector,
facilitating interaction with the surroundings, while the internal torque
governs manipulator motion dynamics and compensates for internal losses. By
estimating external torque via the difference between measured joint torque and
internal losses, we implement a sensorless control strategy which is
demonstrated through a peg-in-hole task. Lastly, a learning-based motion
planner based on the hybrid dynamic model assists in planning time-efficient
trajectories for the manipulator. This comprehensive approach underscores the
efficacy of integrating physics-based and data-driven models for advanced
manipulator control and planning in industrial environments.
