Admittance control of the robot is an important method to improve human–robot collaborative performance. However, it displays poor matching between admittance parameters and human–robot collaborative motion. This results in poor motion performance when the robot interacts with the changeable environment (human). Therefore, to improve the performance of human–robot collaboration, the human-like variable admittance parameter regulator (HVAPR) based on the change rate of interaction force is proposed by studying the human arm’s static and dynamic admittance parameters in human–human collaborative motion. HVAPR can generate admittance parameters matching with human collaborative motion. To test the performance of the proposed HVAPR, the human–robot collaborative motion experiment based on HVAPR is designed and compared with the variable admittance parameter regulator (VAPR). The satisfaction, recognition ratio, and recognition confidence of the two admittance parameter regulators are statistically analyzed via questionnaire. Simultaneously, the trajectory and interaction force of the robot are analyzed, and the performance of the human–robot collaborative motion is assessed and compared using the trajectory smoothness index and average energy index. The results show that HVAPR is superior to VAPR in human–robot collaborative satisfaction, robot trajectory smoothness, and average energy consumption.

Robots with human-like appearances and structures are usually well accepted in the human–robot interaction. However, compared with human-like appearances and structures, the human-like motion plays a much more critical role in improving the efficiency and safety of the human–robot interaction. This paper develops a human-like motion planner based on human arm motion patterns (HAMPs) to fulfill the human–robot object handover tasks. First, a handover task is divided into two sub-tasks, that is, pick-up and delivery, and HAMPs are extracted for these two sub-tasks separately. The resulting HAMPs are analyzed, and a method is proposed to select HAMPs that can represent the characteristics of the human arm motion. Then the factors affecting the duration of the movement primitives are analyzed, and the relationship between the duration of the movement primitives and these factors is determined. Based on the selected HAMP and the computed duration of the movement primitives, a human-like motion planning framework is developed to generate the human-like motion for the robotic arms. Finally, this motion planner is verified by the human–robot handover experiments using a KUKA IIWA robot. It shows that the resulting trajectories can correctly reflect the relative relationship between the joints in the human arm motion and are very close to the recorded human arm trajectories. Furthermore, the proposed motion planning method is compared with the motion planning method based on minimum total potential energy. The results show that the proposed method can generate more human-like motion.

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