Tiago Veiga
Papers
6
Total Citations
143
H-Index
4
About
Tiago Veiga is a leading researcher in autonomous robotics, specializing in decision-theoretic planning under uncertainty. His work centers on enabling robots to act intelligently in complex, dynamic environments by leveraging Partially Observable Markov Decision Processes (POMDPs). A key contribution is the development of "information reward" frameworks, which allow robots to actively seek out information to reduce uncertainty—a critical capability for tasks like cooperative perception in networked robot systems. His seminal 2014 paper on this topic has garnered 64 citations, establishing a foundation for active perception. Veiga has also advanced semantic mapping, using probabilistic logic to help robots efficiently search for objects in domestic settings (18 citations) and maintain accurate maps of changing environments (12 citations). His hierarchical approach to active semantic mapping represents a significant step toward truly autonomous domestic robots. Beyond perception, he has explored multimodal human-robot interaction, integrating user state estimation into decision-making. As a core member of the SocRob project, Veiga’s work bridges theoretical planning algorithms with practical robotic systems, directly contributing to the development of robots that can perceive, reason, and act in the real world.
Research Focus
Key Achievements
Top Papers
- 1
- 2Active cooperative perception in network robot systems using POMDPs44 citations · 2010
- 3
- 4
- 5SocRob@Home4 citations · 2019
- 6