Marcus V. D. Veloso
Papers
2
Total Citations
95
H-Index
2
About
Marcus V. D. Veloso is a robotics researcher whose work lies at the intersection of neural network learning, short-term memory mechanisms, and robotic navigation. His most cited paper (2009, 89 citations) investigates how short-term memory influences neural classifiers in robot navigation tasks, specifically focusing on the wall-following strategy—a foundational behavior for autonomous mobile robots. This work provides critical insights into how memory-augmented neural networks can improve real-time decision-making in dynamic environments, bridging cognitive science and practical robotics. Veloso also contributed to the development of SOM4R (2017), a middleware for robotic applications built on a resource-oriented architecture, demonstrating his interest in scalable, interoperable software frameworks for robotics. While his citation counts are modest, his research addresses core challenges in robot learning and navigation, offering practical solutions for memory-constrained systems. Veloso’s work is particularly valuable for students and researchers exploring the integration of neural networks with robotic control, as it highlights the trade-offs between memory capacity and task performance in autonomous systems.
Research Focus
Key Achievements
Top Papers
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