Camilo Gordillo

Papers

Total Citations

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About

Camilo Gordillo is a robotics researcher whose work lies at the intersection of deep reinforcement learning and autonomous manipulation, with a particular focus on enabling robots to master complex, real-world physical tasks. His most cited paper, "Learning to Pour using Deep Deterministic Policy Gradients" (2018, 28 citations), addresses a fundamental yet challenging skill: teaching robots to pour liquids accurately to specific heights without spilling. By applying deep deterministic policy gradients, Gordillo demonstrated how robots can learn to handle the complex, non-linear dynamics of fluids—a problem that traditional control methods struggle to solve. This work highlights his broader contribution to advancing robot learning in unstructured environments, where precise, adaptive behavior is critical. Gordillo’s research not only pushes the boundaries of robotic dexterity but also has practical implications for domestic service robots and industrial automation. With his focus on sample-efficient learning and real-world deployment, he is helping bridge the gap between simulated training and physical robot performance, making him a notable emerging voice in the field of intelligent robotic manipulation.