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About

Angel J. Valencia is a leading researcher in the intersection of robotics, computer vision, and deformable object manipulation. His work focuses on the critical challenge of enabling robots to perceive, model, and autonomously interact with non-rigid materials—a frontier essential for automating delicate tasks in manufacturing, healthcare, and logistics. Valencia’s major contributions include pioneering methods for real-time 3D shape tracking of deformable objects, which allows robots to adapt their manipulation strategies on the fly. He has also advanced the field by developing novel approaches that combine self-organizing maps with graph neural networks to model complex elastic deformations, as well as physics-informed neural networks for predicting object motion. His highly cited survey on multi-modal sensing and robotic manipulation of non-rigid objects (69 citations) serves as a foundational reference for the community. Additionally, his work on optimal grasp planning for vacuum grippers (26 citations) addresses practical challenges in industrial automation. With recent publications pushing the boundaries of deformation modeling, Valencia continues to shape how robots can safely and dexterously handle the soft, dynamic objects that fill our world.