Renato R. da Silva
Papers
2
Total Citations
18
H-Index
2
About
Renato R. da Silva is a researcher whose work sits at the compelling intersection of robotics, artificial intelligence, and social cognition. His primary research areas focus on developing sociable robots—embodied agents capable of recognizing humans and engaging in meaningful social interactions. Da Silva’s major contributions lie in advancing how robots learn and represent knowledge for shared attention, a foundational skill for human-robot collaboration. In his 2009 paper on relational reinforcement learning, he introduced an enhanced version of the TG algorithm (ETG) that uses an incremental learning process during episodes, eliminating the need for secondary memory to improve efficiency in social interactive simulations. His 2008 work on hybrid knowledge representation further demonstrated how robotic architectures can be streamlined to reduce the time and effort required to build socially adept machines. While his citation counts (10 and 8, respectively) reflect a focused, early-career impact, these papers represent foundational steps in enabling robots to participate in a heterogeneous society of humans and machines. Da Silva’s research offers valuable insights for students and researchers interested in the practical challenges of creating robots that can learn, adapt, and share attention in real-world social contexts.
Research Focus
Key Achievements
Top Papers
- 1Relational reinforcement learning applied to shared attention10 citations · 2009
- 2