Sebin and Jumin presented their paper, “Visual-RRT: Finding Paths toward Visual-Goals via Differentiable Rendering” at CVPR 2026, held from June 3rd – 7th, 2026, in Denver, USA.
Visual-RRT (vRRT) is a novel robot motion planning technology that enables robots to plan paths based solely on a target image or visual observations, eliminating the need for explicit numeric target joint values required by traditional RRT planners. By combining sampling-based RRT exploration with differentiable robot rendering, vRRT searches for paths that minimize the visual discrepancy between the target image and the current rendered robot state. The method enhances exploration efficiency through a frontier-based strategy that prioritizes visually promising nodes and an inertial gradient tree expansion technique that leverages prior optimization data.