Morris Gu
Papers
5
Total Citations
235
H-Index
3
About
Morris Gu is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction. His primary contributions center on **robotic grasping and manipulation**, where his comprehensive survey on deep learning approaches to grasp synthesis has become a foundational reference, amassing over 215 citations. This work systematically mapped the rapid progress in applying neural networks to the challenge of enabling robots to securely pick up objects. Beyond grasping, Gu has pioneered work in **explainable AI for robotics**, notably through his 2025 study on making Learning from Demonstration (LfD) systems more interpretable for novice users. He has also advanced **multimodal perception** by integrating high-resolution tactile sensing with vision to improve grasp stability prediction, and developed novel **augmented reality interfaces** for intuitive robot navigation. By bridging deep learning, tactile sensing, and user-centered design, Gu’s research is shaping a future where robots are not only more dexterous but also more transparent and accessible to non-experts.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Approaches to Grasp Synthesis: A Review215 citations · 2023
- 2AR Point &Click: An Interface for Setting Robot Navigation Goals9 citations · 2022
- 3Deep Learning Approaches to Grasp Synthesis: A Review7 citations · 2022
- 4
- 5