Abstract: Manual ash content detection of flotation tailings in coal preparation plants has a low degree of automation and cannot meet the requirements of online, rapid, and accurate detection. Applying a robotic manipulator to flotation tailings ash content detection improves detection efficiency and safety. To address the problems of motion stuttering and insufficient compliance of the robotic manipulator during operation, an improved Task-Joint Space Dynamic Adaptive Compliance Control (TJS-DACC) algorithm was proposed. In this algorithm, a reinforcement learning framework was introduced into TJS-DACC, and the response speed and acceleration of the manipulator end effector were comprehensively considered to construct a multi-objective fused reward function. Meanwhile, penalty and loss functions were designed, and an optimization model for the interpolation weight factor "α" was established to achieve adaptive fusion of task-space and joint-space control of the manipulator. Matlab simulation experiments and physical platform experiments were conducted to verify that, when controlled by the improved TJS-DACC algorithm, the sampling efficiency of the flotation tailings ash content detection manipulator increased by 26.13% and 15.03%, respectively, compared with those under the traditional joint-space PID algorithm and the TJS-DACC algorithm. Moreover, the trajectory was continuous and smooth, joint coordination was strong, and no emergency stops, stuttering, or impact phenomena occurred, indicating that the control performance is superior to that of the comparison algorithms.