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About

Antoine Richard is a robotics researcher whose work spans autonomous manipulation, space robotics, and machine learning for control systems. His research addresses some of the most demanding frontiers in robotics: enabling robots to operate reliably in unstructured and extreme environments, from factory floors to the lunar surface. Richard's most impactful contribution to date is **GraspLDM** (2024, 15 citations), which applies latent diffusion models to generate diverse, task-relevant 6-DoF robotic grasps for unknown objects — a significant advance in generalizable manipulation. Complementing this, his work on **OmniLRS** (2024, 13 citations) delivers a photorealistic simulator purpose-built for lunar robotics, directly supporting the renewed global interest in lunar exploration under the Artemis Accords. His investigations into multi-robot mesh networks (11 citations) and 3D Gaussian Splatting for dynamic object tracking (9 citations) further demonstrate his breadth across perception, communication, and autonomy. Earlier work benchmarking neural network architectures for system identification laid important groundwork for data-driven robot modeling. Across his career, Richard has accumulated over 60 citations, establishing himself as an emerging voice in the intersection of generative AI, space robotics, and autonomous systems.