Samuel Temporao
Papers
1
Total Citations
2
H-Index
1
About
Samuel Temporao is a researcher at the forefront of intelligent robotics, specializing in motion planning and reinforcement learning (RL). His work addresses a critical challenge in autonomous navigation: enabling mobile robots to move safely and efficiently from point A to point B in dynamic environments. Temporao’s major contribution is the development of a novel RL-based local motion planning (RL-LMP) approach, introduced in his most-cited paper from 2020. This method uniquely combines a training stage, where the robot learns optimal navigation policies in simulation, with an online stage for real-time deployment on physical platforms. By leveraging RL, his approach allows robots to adapt to obstacles and path deviations without relying on pre-programmed rules, significantly improving robustness over traditional planners. While his citation count is modest, the work has been recognized for its practical potential in both virtual and real-world mobile robotics. Temporao’s research bridges the gap between theoretical RL algorithms and tangible robotic applications, offering a scalable solution for autonomous systems in logistics, service robotics, and beyond. His contributions are a stepping stone toward more adaptive, learning-driven navigation in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Improving Local Motion Planning with a Reinforcement Learning Approach2 citations · 2020