Papers
15
Total Citations
185
H-Index
8
About
Derya Aksaray is a leading researcher in robotics and artificial intelligence, specializing in the intersection of natural language processing, formal methods, and control synthesis. Her work focuses on enabling robots to efficiently understand and ground abstract spatial concepts from natural language instructions, bridging the gap between human communication and robotic action. A major contribution is her development of models that allow mobile robots to learn unknown groundings for language interaction, with her seminal 2018 paper on this topic garnering 59 citations. She has also made significant advances in resilient control, introducing metrics for temporal relaxation of Signal Temporal Logic (STL) specifications to ensure robust robot behavior even when tasks become infeasible. Her research extends to reinforcement learning for satisfying temporal logic objectives, with multiple highly cited papers (14+ citations each) on robust satisfaction and tractable learning of STL specifications. Aksaray has also explored human-robot teaming, quantifying human decision-making to improve bidirectional communication. Her work on distributed planning for cooperative tasks and informative path planning under temporal logic constraints further demonstrates her impact, with her most cited papers collectively accumulating over 170 citations.
Research Focus
Key Achievements
Top Papers
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- 6Tractable Reinforcement Learning of Signal Temporal Logic Objectives13 citations · 2020
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