Greg Mori
Papers
15
Total Citations
534
H-Index
9
About
Greg Mori is a prominent computer vision and robotics researcher whose work spans human-robot interaction, deep learning, and autonomous navigation. His research has made significant contributions to how machines perceive, interpret, and respond to human behavior in complex, real-world environments. Mori's foundational work in multi-robot systems revolutionized natural human-robot interaction, demonstrating that uninstrumented humans could select, group, and command robot teams using only gaze, gesture, and speech — eliminating the need for specialized controllers or wearable devices. This body of work, spanning from 2010 through 2014 and accumulating over 200 collective citations, established intuitive vision-based frameworks for directing both ground robots and UAV teams in dynamic settings. His more recent research has pushed into deep learning efficiency and autonomous navigation. His 2018 paper on in-parallel neural network pruning and quantization (138 citations) tackled the critical challenge of deploying large models on resource-constrained hardware. His 2020 work on relational graph learning for crowd navigation (146 citations) demonstrated how graph convolutional networks and model-based reinforcement learning can enable robots to navigate safely among pedestrians by reasoning about inter-agent relationships. Together, Mori's contributions reflect a coherent vision: building intelligent systems that interact seamlessly and efficiently with both people and their environments.
Research Focus
Key Achievements
Top Papers
- 1Relational Graph Learning for Crowd Navigation146 citations · 2020
- 2Deep Neural Network Compression by In-Parallel Pruning-Quantization138 citations · 2018
- 3
- 4
- 5Selecting and Commanding Individual Robots in a Multi-Robot System26 citations · 2010
- 6
- 7
- 8Selecting and commanding groups in a multi-robot vision based system14 citations · 2011
- 9"You are green"9 citations · 2014
- 10