Aiden Swann

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About

Aiden Swann is a rising star in robotic perception and safe autonomy, whose work bridges the gap between vision, touch, and real-time navigation. His research centers on integrating **3D Gaussian Splatting (3DGS)** with tactile sensing and control theory to create more capable and safer robotic systems. In his highly cited work *"Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting"* (2024, 9 citations), Swann pioneered a method to supervise 3DGS scenes using optical tactile sensors, enabling robots to build richer, multi-modal object representations that combine visual geometry with physical contact data. He extended this foundation with *"A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps"* (2025, 5 citations), introducing SAFER-Splat—a real-time safety filter that uses control barrier functions to ensure collision-free navigation within dynamically constructed GSplat maps. Most recently, his *"Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting"* (2025, 2 citations) proposes an active sensing framework that intelligently selects where to look and touch next, optimizing scene reconstruction. Swann’s work is notable for its practical, minimally invasive approach to integrating perception and safety, making him a key figure in the next generation of embodied AI research.