David Colliaux

Sony (France), Sony Computer Science Laboratories

Papers

Total Citations

H-Index

About

David Colliaux’s research lies at the intersection of robotics, artificial intelligence, and computational biology, with a focus on enabling autonomous systems to explore and interact with complex, high-dimensional environments. His most cited work, “Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces” (2020, 32 citations), introduces a novel architecture that combines deep neural networks for unsupervised feature learning with shallow online learning, allowing a microfarming robot to generate curiosity-driven, goal-directed behaviors. This contribution advances the field of developmental robotics by demonstrating how intrinsic motivation and episodic memory can guide exploration in real-world, high-dimensional sensory spaces, such as those encountered in agricultural settings. Colliaux also contributed the “ARABIDOPSIS 3D+T dataset” (2021, 2 citations), a spatiotemporal point cloud dataset of Arabidopsis plants captured using robotic imaging, which supports research in plant phenotyping and 3D growth modeling. His work bridges robotics and plant science, offering tools for automated monitoring and analysis. With growing citation impact, Colliaux’s research is shaping how robots learn and adapt in unstructured environments, making him a notable figure in embodied AI and bio-inspired robotics.