Research on the opening method of robotic arm based on force feedback reinforcement learning

In practical applications involving robotic arms, particularly in tasks such as manipulating door handles, improper strategies often lead to excessive contact forces. Such forces not only jeopardize the integrity of the robotic arm’s joints but also pose a risk of damaging the door handle. This paper delves into a meticulous study aimed at refining the opening techniques employed by manipulators, enhancing their adaptability across various environments. A novel method is introduced, amalgamating force information feedback with the deep deterministic policy gradient algorithm, fostering a more nuanced approach in trajectory planning. This innovative strategy is meticulously evaluated through simulations and physical experiments, proving instrumental in guiding the robotic arm toward the successful completion of the door-opening task. The findings from the experiments underscore the algorithm’s prowess in cultivating a compliant door-opening strategy, harmonizing with the force applied at the manipulator’s end. A comparative analysis with conventional methods reveals a notable reduction in the end force of the manipulator, facilitating a more efficient and secure execution of door-opening operations.