Andrew Silva
Papers
14
Total Citations
177
H-Index
8
About
Andrew Silva’s research sits at the intersection of human-robot interaction, reinforcement learning, and multi-agent systems, with a focus on making autonomous agents both capable and trustworthy. His most impactful work, “Multi-UAV planning for cooperative wildfire coverage and tracking with quality-of-service guarantees” (47 citations), pioneers the coordination of drone teams for real-time wildfire monitoring, directly addressing safety-critical challenges in emergency response. He is also a leading voice in integrating human expertise into AI, as seen in his highly cited paper “Encoding Human Domain Knowledge to Warm Start Reinforcement Learning” (28 citations), which demonstrates how to leverage expert knowledge to accelerate deep RL training—a concept he has extended through neural encoding and natural language specification. Silva’s contributions to human-robot teaming include the first interruptibility-aware mobile robot system (20 citations), which uses social cues to decide when to interact with people, and work on interpretable policies for autonomous driving (13 citations). His recent research on adaptive personalized explainability (2024) further advances transparent AI. With over 160 total citations, Silva’s work is shaping how robots learn from and collaborate with humans in high-stakes environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Encoding Human Domain Knowledge to Warm Start Reinforcement Learning28 citations · 2021
- 3Robot Classification of Human Interruptibility and a Study of Its Effects20 citations · 2018
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- 5Learning Interpretable, High-Performing Policies for Autonomous Driving13 citations · 2022
- 6Safe Coordination of Human-Robot Firefighting Teams11 citations · 2019
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