The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement.