Peiyi Shen
Papers
8
Total Citations
427
H-Index
6
About
Peiyi Shen is a leading researcher in human-robot interaction and intelligent sensing, whose work bridges computer vision, multimodal perception, and autonomous robotics. His core contributions center on enabling machines to understand and respond to human actions through advanced gesture and action recognition systems. Shen’s most influential work, “Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM” (277 citations), pioneered a fusion of 3D convolutional networks with LSTM architectures to capture spatiotemporal dynamics in gestures, setting a benchmark for multimodal interaction. He further advanced practical human-robot collaboration with his online continuous action recognition algorithm using Kinect sensors (57 citations), which models actions as sequences of key poses and atomic motions—a framework that has been widely adopted in assistive robotics. Shen also contributed to semantic scene understanding from depth images and real-time whole-body motion imitation for humanoid robots, demonstrating a consistent focus on making robots spatially aware and responsive. His work on fast robot identification and mapping (23 citations) underscores his impact on autonomous navigation in IoT environments. Through these innovations, Shen has shaped how robots perceive, learn from, and safely interact with humans in dynamic settings.
Research Focus
Key Achievements
Top Papers
- 1Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM277 citations · 2017
- 2
- 3Semantic scene completion with dense CRF from a single depth image33 citations · 2018
- 4A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor23 citations · 2015
- 5Fast human whole body motion imitation algorithm for humanoid robots22 citations · 2016
- 6Human action recognition using key poses and atomic motions12 citations · 2015
- 7A NEW METHOD FOR MOVING OBJECT TRACKING WITH MULTI-ROBOT2 citations · 2011
- 8Spatial Understanding as a Common Basis for Human-Robot Collaboration1 citations · 2017