Bruno Fernandes
Papers
6
Total Citations
62
H-Index
4
About
Bruno Fernandes is a researcher at the intersection of reinforcement learning, computer vision, and human-robot interaction, with a focus on developing robust, efficient, and socially aware autonomous systems. His most cited work, "A Robust Approach for Continuous Interactive Actor-Critic Algorithms" (2021, 23 citations), addresses a critical challenge in reinforcement learning: enabling agents to adapt to dynamic or disturbed environments, thereby improving policy stability in real-world applications. Fernandes has also made significant contributions to fire recognition, pioneering lightweight and efficient deep learning models, such as the octave convolutional neural network (2019, 13 citations) and optimized convolution approaches for fire classification (2022, 11 citations), which are designed for deployment on hardware-constrained systems like mobile devices and embedded robotics. Extending his work to social robotics, he explores proxemic behavior in navigation tasks (2022, 10 citations), using reinforcement learning to ensure that robots respect personal space during human interaction. His broader portfolio includes comparative studies of humanoid robot simulators and calibration-free eye-tracking algorithms, demonstrating a commitment to advancing both the theoretical and practical foundations of intelligent, interactive machines.
Research Focus
Key Achievements
Top Papers
- 1A Robust Approach for Continuous Interactive Actor-Critic Algorithms23 citations · 2021
- 2
- 3Convolution Optimization in Fire Classification11 citations · 2022
- 4Proxemic behavior in navigation tasks using reinforcement learning10 citations · 2022
- 5A Comparison of Humanoid Robot Simulators: A Quantitative Approach3 citations · 2020
- 6