Thiago Pedro Donadon Homem
Papers
14
Total Citations
154
H-Index
8
About
Thiago Pedro Donadon Homem is a leading researcher in cognitive robotics and artificial intelligence, with a primary focus on humanoid robot soccer. His work centers on developing intelligent, autonomous systems capable of perceiving and acting in dynamic environments, particularly within the competitive RoboCup Humanoid KidSize League. Homem’s major contributions include pioneering the application of deep reinforcement learning to enable humanoid robots to play soccer, and developing qualitative case-based reasoning and learning algorithms that allow robots to adapt and improve their behavior over time. His most cited paper (42 citations) introduces a novel framework for qualitative case-based reasoning and learning, while his work on monocular vision systems (17 citations) has been instrumental in enabling robots to track balls, identify goals, and recognize teammates and opponents using a single camera. Homem also led the design and assembly of the Newton humanoid robot, a platform specifically engineered for RoboCup competition and cognitive robotics research. His research on using reinforcement learning to improve walking stability on sloped terrain (11 citations) demonstrates his commitment to real-world robot adaptation. With over 140 total citations across his publications, Homem has established himself as a key innovator in bringing machine learning and cognitive architectures to physically embodied robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Qualitative case-based reasoning and learning42 citations · 2020
- 2A Single Camera Vision System for a Humanoid Robot17 citations · 2014
- 3Deep Reinforcement Learning for a Humanoid Robot Soccer Player13 citations · 2021
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- 5Humanoid Robot Framework for Research on Cognitive Robotics12 citations · 2018
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