Hanne Say
Papers
2
Total Citations
7
H-Index
2
About
Hanne Say is a pioneering researcher at the intersection of cognitive science and robotics, specializing in lifelong learning, multi-task reinforcement learning, and skill transfer. Her work draws profound inspiration from human brain and behavior to develop novel control and learning methods for autonomous systems. In her highly cited 2024 paper, "Bidirectional Progressive Neural Networks With Episodic Return Progress for Emergent Task Sequencing and Robotic Skill Transfer," she introduced a groundbreaking framework that mimics how humans acquire knowledge and transfer skills across tasks, achieving 4 citations in its first year. Her 2023 work, "A Model for Cognitively Valid Lifelong Learning," tackles the critical challenges of catastrophic forgetting and knowledge transfer in continual learning, proposing metrics that go beyond traditional performance measures. Say’s contributions are reshaping how robots learn sequentially and adaptively, bridging the gap between artificial and human cognition. Her research has significant implications for developing more flexible, intelligent robotic systems capable of mastering complex, real-world task sequences.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Model for Cognitively Valid Lifelong Learning3 citations · 2023