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About

David Blanco–Mulero is a leading researcher in robotic manipulation, with a primary focus on the notoriously difficult domain of deformable objects—particularly textiles. His work addresses the core challenges of cloth manipulation, where high-dimensional configuration spaces and complex, material-dependent dynamics defy traditional rigid-object approaches. His most impactful contribution, "Learning Visual Feedback Control for Dynamic Cloth Folding" (2022, 31 citations), pioneers a method for robots to perform dynamic folding tasks, such as manipulating a square piece of fabric in motion, using visual feedback. This work is complemented by his comprehensive "Unfolding the Literature: A Review of Robotic Cloth Manipulation" (2024, 20 citations), which has become a key reference for the community. Blanco–Mulero also developed QDP (2023, 13 citations), a framework that sequentially optimises quasi-static and dynamic manipulation primitives, adapting to varying cloth properties like stiffness and density. Beyond textiles, his earlier work on a real-time reconfigurable collision avoidance system (2017, 8 citations) demonstrates a broader interest in safe, adaptive robotics for industrial settings. His recent co-adaptation research (2023) explores the exciting frontier of robots that learn both their design and behaviour through imitation, promising to eliminate costly manual engineering. With a growing citation footprint, Blanco–Mulero is a rising star shaping the future of deformable object manipulation.