Giving robots the human sense of touch
Make robots safer, more intelligent, and more versatile in the human environment.
GTac and GTac Families
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Giving robots with human sense of touch and humans-skill transfer:
•Tactile Sensors
•Rich feedback, integrability, and customizability
•Multi-modal, modular, and skin-inspired for larger and smaller area
•Robotic Integration
•Corelated with tactile sensors design, hand design and robotic tasks
•Robotic Tasks
•Leverage large amount of tactile feedback
•Imitation learning
GTac-Families
Interaction Control for Tool Manipulation on Deformable Objects Using Tactile Feedback
Hanwen Zhang*, Zeyu Lu*, Wenyu Liang, Haoyong Yu, Yao Mao, and Yan Wu
* indicates equal contributions
(Published in IEEE RA-L)
Wearable Robotics, Human Gait Analysis, and Pattern Recognition
(Prior to my Ph.D.)
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Human Locomotion Recognition using IMU and Deep Learning for Exoskeleton
Zeyu Lu, Ashwin Narayan, and Haoyong Yu, “A Deep Learning Based End-to-End Locomotion Mode Detection Method for Lower Limb Wearable Robot Control,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 4091–4097.
Suspended Backpack Design and Gait Analysis and Modeling
Y. Leng, X. Lin, Z. Lu, A. Song, Z. Yu, and C. Fu, “A model to predict ground reaction force for elastically-suspended backpacks,” Gait & Posture, vol. 82, pp. 118–125, 2020
Patent: CN210841945U
Mosquito pattern recognition and localization for automating vaccine production
Supervised by Prof. Gregory Chirikjian
at Johns Hopkins University, Baltimore, USA
I would like to extend my heartfelt thanks to my supervisor, collaborators, and colleagues. Their unwavering support and invaluable guidance have been essential in making these projects possible. I truly appreciate every contribution that helped bring these works to fruition.