SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
2026-07-09 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a system called SplatCtrl to help robots move safely in changing environments they haven't seen before. It uses a special way to build a 3D map of the surroundings from camera data and quickly update it when things move. Then, they create smooth estimates of how close the robot is to obstacles to avoid collisions. This lets the robot react in real time and move smoothly without bumping into things. They tested it in simulations, on real robots, and around humans, showing it works well in uncertain and dynamic settings.
robotic manipulators3D Gaussian SplattingRGB-D streamsscene reconstructionsigned distance functionscontrol barrier functionscollision avoidancereactive robot controlimplicit representationreal-time motion generation
Authors
Siddarth Jain, Ho Jin Choi
Abstract
Robotic manipulators excel in structured environments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB-D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision probability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance metrics are incorporated into control barrier functions, resulting in a unified perception-action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human-robot workspace demonstrates the framework's effectiveness, achieving integrated scene reconstruction and reactive control in uncertain, and dynamic environments.