Miguel Silva
Papers
1
Total Citations
12
H-Index
1
About
Miguel Silva is a leading researcher in robotics and artificial intelligence, with a primary focus on active semantic mapping, probabilistic reasoning, and decision-making under uncertainty. His most cited work, "A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs" (2021, 12 citations), addresses the critical challenge of enabling mobile agents to maintain accurate semantic maps in complex, dynamic environments. Silva’s major contribution lies in integrating probabilistic logic with Partially Observable Markov Decision Processes (POMDPs) to create a hierarchical framework that balances exploration and exploitation, allowing robots to intelligently resolve uncertainty from noisy perception and unexpected environmental changes. This work has significant implications for autonomous navigation, search-and-rescue, and domestic robotics. Although early in his career, Silva’s innovative approach to active perception and information-theoretic rewards has already garnered attention, positioning him as a rising authority in intelligent robotic systems. His research continues to push the boundaries of how machines understand and interact with the world, promising impactful advances in autonomous decision-making.
Research Focus
Key Achievements
Top Papers
- 1