This study proposes a model predictive path integral (MPPI) control method that combines adaptive covariance adjustment with low-pass perturbation filtering for the pose control of a robotic manipulator. The proposed method updates the sampling covariance according to the position and orientation errors of the tool center point, which allows a large sampling range during the approach phase and reduces the perturbation magnitude near the target pose. The sampled perturbation sequences are then passed through a low-pass filter before rollout evaluation, and the nominal control sequence is updated using the weighted sum of the filtered perturbations. This structure adaptively regulates the sampling range while reducing abrupt temporal variations in the sampled perturbation sequences. Numerical simulations were conducted on a 6-DoF robotic manipulator under a sequential target-pose scenario with five random seeds. The proposed MPPI method reached the final target-hold condition at 4.10 ± 0.08 s and maintained root-mean-square (RMS) position and orientation errors of 0.47 ± 0.12 mm and 0.05 ± 0.01°, respectively, with an RMS input variation of 0.02 ± 0.001 rad/s. These results indicate that the proposed method can maintain fast target-approach performance while improving near-goal stability and input smoothness in robotic manipulator pose control.
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