<p>Recent studies have suggested ways to enhance user perception of remote workspaces and robot autonomy, often at the expense of system suitability and efficiency. This research introduces a novel method, Distributed Supervisory Control (DSC), leveraging Virtual Reality (VR) to enhance teleoperation. The DSC method intelligently distributes tasks between the robot and the human operator, minimizing shared autonomy conflicts and sensory data transfer. It uses Deep Reinforcement Learning (DRL) through a Twin Delayed Deep Deterministic Policy Gradient (TD3) method for tasks like obstacle avoidance. Real-time experimentation validated the method’s effectiveness through performance metrics, including NASA-TLX, task execution time, and obstacle collision frequency. Kruskal-Wallis tests identified significant differences in task execution times and collision frequency across DSC, Direct Control (DC), and Assistive Direct Control (ADC). Dunn’s post-hoc tests indicated DSC significantly outperformed DC and ADC in both execution time and collision frequency. Similarly, NASA-TLX scores for effort, mental demand, performance, frustration, physical demand, and temporal demand also showed significant differences (<i>p</i> &lt; 0.05), supporting DSC’s lower task load. User case studies revealed enhanced user experience, as measured by the System Usability Scale (SUS) and Igroup Presence (IPQ) questionnaires for immersive experience. The non-parametric Kruskal-Wallis test was utilized to verify significant differences between group medians, followed by a Dunn’s test that revealed significant performance improvements with our Distributed Supervisory Control (DSC) method relative to Direct Control (DC) and Assistive Direct Control (ADC), thereby enhancing task efficiency and overall interface suitability. In conclusion, Distributed Supervisory Control significantly enhances task efficiency and user experience in robot teleoperation environments, demonstrating its potential as a new standard for remote operations.</p>