Optimize arm reaching

During the process of reaching a target, a robotic arm may generate a large number of redundant movements due to the stochastic nature of planning algorithms. We think that each joint of the robotic arm has a unique functionality towards the arm’s end effector. Therefore, during motion planning, we optimized the weights of each joint based on its individual functionality. We introduce the human arm operation mechanism - When a person is reaching for a distant target, the shoulder-near joints take on more actions. We proposed an advanced auxiliary loss function. By utilizing this function, we can filter out multiple solutions from the inverse model, thus reducing the amount of joint change and redundant movements. We conducted comprehensive comparative experiments on the UR3 robotic arm, and the results showed that our approach achieved better efficiency and outcomes.

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