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This paper presents a new coordination manipulation strategy for a custom-made dual-arm robot. With master and slave coordination infrastructure, both spatial relation and sense of touch are considered to hold an object stably. Given the known trajectory of the master arm, the slave arm fuses position and force commands by using the Kalman filter to yield optimal compensation amounts. The proposed strategy has been experimentally evaluated, and the results confirm that it was capable of dealing with fragile and flexible objects. In addition, the influence of the loop time of the digital controller on force control for this task was also investigated in mathematical and simulated ways. Furthermore, a series of experiments were designed to explore the effects that have influences on errors in force control. The main factors that affect force control error were analyzed.

This article reports the construction of an articulated manipulator’s hybrid dynamic model and trajectory planning and optimization of the manipulator using deep reinforcement learning (RL) on the dynamic model. The hybrid model was composed of a physical-based reduced-order dynamic model, linear friction and damping terms, and a deep neural network model to compensate for the nonlinear characteristics of the manipulator. The hybrid model then served as the digital twin of the manipulator for trajectory planning to optimize energy efficiency and operation speed by using RL while taking obstacle avoidance into consideration. The proposed strategy was simulated and experimentally validated. The energy consumption along paths was reduced and the speed was increased so the manipulator could achieve more efficient motion.