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About

Tiago Silva is a robotics researcher whose work sits at the intersection of machine learning, humanoid locomotion, and competitive multi-agent systems. His primary focus is on enabling humanoid robots to perform complex, dynamic skills—particularly kicking while in motion—for the RoboCup 3D simulation soccer league. His most cited work (2020, 20 citations) introduces a reinforcement learning approach using Proximal Policy Optimization (PPO) to generate fast, reliable kicking behaviors for moving robots, a significant step toward more agile and game-realistic robotic soccer. Silva further advances this line of research with contributions to 6D localization and integrated kicking (2021, 11 citations), and explores the full pipeline from low-level motor control to high-level strategic decision-making (2019). As a key member of the FC Portugal 3D Simulation Team, he helps bridge the gap between simulated and real-world robot performance, emphasizing model-based transferability. Though his citation counts are still growing, his work is foundational for researchers aiming to combine deep reinforcement learning with real-time physical constraints in humanoid robotics.