Surround-view perception is increasingly important for navigation and loco-manipulation, especially in human-in-the-loop settings such as teleoperation, data collection, and emergency takeover. However, current robot visual interfaces often rely on narrow forward-facing views, suffer from motion-induced jitter that causes simulator sickness in head-mounted displays, and require cumbersome manual switching among multiple on-board cameras. We introduce a surround-view robotic vision system that combines six cameras with LiDAR to provide full 360° visual coverage, while meeting the geometric and real-time constraints of embodied deployment. We further present RobotPan, a feed-forward framework that predicts metric-scaled and compact 3D Gaussians from calibrated sparse-view inputs for real-time rendering, reconstruction, and streaming. RobotPan lifts multi-view features into a unified spherical coordinate representation and decodes Gaussians using hierarchical spherical voxel priors, allocating fine resolution near the robot and coarser resolution at larger radii to reduce redundancy without sacrificing fidelity. To support long sequences, our online fusion updates dynamic content while preventing unbounded growth in static regions by selectively updating appearance. Finally, we release a multi-sensor dataset tailored to 360° novel view synthesis and metric 3D reconstruction for robotics, covering navigation, manipulation, and locomotion on real platforms. Experiments show that RobotPan achieves competitive quality against prior feed-forward reconstruction and view-synthesis methods while producing substantially fewer Gaussians, enabling practical real-time embodied deployment.