Alfredo Orozco-de-la-Paz
Papers
1
Total Citations
17
H-Index
1
About
Alfredo Orozco-de-la-Paz is a robotics researcher whose work centers on autonomous navigation, path planning, and the application of reinforcement learning to service robotics. His most-cited paper, "Navigation and path planning using reinforcement learning for a Roomba robot" (2016, 17 citations), marks a foundational contribution to the development of intelligent service robots capable of operating in real-world environments like homes, hospitals, and offices. In this work, he demonstrated how a low-cost robotic platform could learn to navigate using a topological map, bridging the gap between theoretical reinforcement learning algorithms and practical, deployable systems. This research laid the groundwork for subsequent advances in autonomous decision-making for mobile robots, emphasizing adaptability and efficiency. Orozco-de-la-Paz’s contributions are particularly notable for their focus on accessible, scalable solutions—making sophisticated robotic behaviors achievable with off-the-shelf hardware. His work continues to influence researchers exploring learning-based approaches to robotics, especially those aiming to integrate AI with physical systems for everyday assistance.
Research Focus
Key Achievements
Top Papers
- 1Navigation and path planning using reinforcement learning for a Roomba robot17 citations · 2016