Education:
08/2018–01/2022: Ph.D., The University of British Columbia, Canada,
Supervisor: Dr. Clarence W. de Silva, Co-supervisor: Dr. Chenglong Fu12/2019–09/2020 : Visiting Ph.D. student, Massachusetts Institute of Technology, USA, Supervisor: Dr. Neville Hogan.
08/2012–07/2016: Undergraduate student, Tsinghua University, China, Supervisor: Dr. Jing Xu.
Work:
05/2023-Present: Machine Learning Engineer, DiDi Research America, Mountain View, USA
06/2022-04/2023: Scientist, Microsoft, Redmond, USA
Research interests:
Deep learning and transfer learning, computer vision, human-robot interaction, sensor fusion, and predictive control.
Foot placement prediction for assistive walking by fusing sequential 3D gaze and environmental context
K. Zhang et al., “Foot placement prediction for assistive walking by fusing sequential 3D gaze and environmental context,” IEEE Robotics and Automation Letters, pp. 1–1, 2021, doi: 10.1109/LRA.2021.3062003.
[Paper link], [Code and data], [video]
Linked dynamic graph cnn: learning on point cloud via linking hierarchical features
K. Zhang, M. Hao, J. Wang, C. W. de Silva, and C. Fu, “Linked dynamic graph cnn: learning on point cloud via linking hierarchical features,” arXiv:1904.10014 [cs], Apr. 2019.
[Paper link], [Code and data]
A subvision system for enhancing the environmental adaptability of the powered transfemoral prosthesis
K. Zhang et al., “A subvision system for enhancing the environmental adaptability of the powered transfemoral prosthesis,” IEEE Transactions on Cybernetics, pp. 1–13, 2020, doi: 10.1109/TCYB.2020.2978216.
[Paper link], [video]
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
K. Zhang et al., “Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation,” Information Fusion, vol. 93, pp. 268–281, May 2023.
[Paper link], [Code and data]
Gaussian-guided feature alignment for unsupervised cross-subject adaptation
K. Zhang, J. Chen, J. Wang, Y. Leng, C. W. de Silva, and C. Fu, “Gaussian-guided feature alignment for unsupervised cross-subject adaptation,” Pattern Recognition, vol. 122, p. 108332, Feb. 2022.
[Paper link], [Code and data]
Unsupervised cross-subject adaptation for predicting human locomotion intent
K. Zhang, J. Wang, C. W. De Silva, and C. Fu, “Unsupervised cross-subject adaptation for predicting human locomotion intent,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 1–1, 2020.
[Paper link], [Code and data]
Teach biped robots to walk via gait principles and reinforcement learning with adversarial critics
K. Zhang, Z. Hou, C. W. de Silva, H. Yu, and C. Fu, “Teach biped robots to walk via gait principles and reinforcement learning with adversarial critics,” Oct. 2019.
[Paper link], [Code and data], [video]
How does the structure embedded in learning policy affect learning quadruped locomotion
K. Zhang, J. Lee, Z. Hou, C. W. de Silva, C. Fu, and N. Hogan, “How does the structure embedded in learning policy affect learning quadruped locomotion?,” arXiv:2008.12970 [cs], Aug. 2020, Accessed: Oct. 01, 2020.
[Paper link], [video]
Environmental features recognition for lower limb prostheses toward predictive walking
K. Zhang, C. Xiong, W. Zhang, H. Liu, D. Lai, Y. Rong, and C. Fu,“Environmental features recognition for lower limb prostheses toward predictive walking,” IEEE Transactions on Neural Systems and Reha-bilitation Engineering, vol. 27, no. 3, pp. 465–476, Mar. 2019.
[Paper link], [Code and data]
Sequential decision fusion for environmental classification in assistive walking
K. Zhang, W. Zhang, W. Xiao, H. Liu, C. W. D. Silva, and C. Fu, “Sequential decision fusion for environmental classification in assistive walking,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 9, pp. 1780–1790, Sep. 2019.
[Paper link], [Code and data]
Jamming analysis and force control for flexible dual peg-in-hole assembly
K. Zhang, J. Xu, H. Chen, J. Zhao, and K. Chen, “Jamming analysis and force control for flexible dual peg-in-hole assembly,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 1930–1939, 2019.
Force control for a rigid dual peg-in-hole assembly
K. Zhang, M. Shi, J. Xu, F. Liu, and K. Chen, “Force control for a rigid dual peg-in-hole assembly,” Assembly Automation, vol. 37, no. 2, pp. 200–207, Apr. 2017.
Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking: a survey
K. Zhang, C. W. de Silva, and C. Fu, “Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking: a survey,” arXiv:1903.07674 [cs], Mar. 2019.
Directional pointnet: 3D environmental classification for wearable robotics
K. Zhang, J. Wang, and C. Fu, “Directional pointnet: 3D environmental classification for wearable robotics,” arXiv:1903.06846 [cs], Mar. 2019.
[Paper link], [Code and data]
J. Xu, Z. Hou, W. Wang, B. Xu, K. Zhang, and K. Chen, “Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks,” IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1658–1667, Mar. 2019. [Paper link]
J. Wang and K. Zhang, “Unsupervised domain adaptation learning algorithm for rgb-d staircase recognition,” arXiv:1903.01212 [cs], Mar. 2019. [Paper link]
Z. Hou, M. Philipp, K. Zhang, Y. Guan, K. Chen, and J. Xu, “The learning-based optimization algorithm for robotic dual peg-in-hole assembly,” Assembly Automation, vol. 38, no. 4, pp. 369–375, Sep. 2018. [Paper link]
Z. Hou, H. Dong, K. Zhang, Q. Gao, K. Chen, and J. Xu, “Knowledge-driven deep deterministic policy gradient for robotic multiple peg-in-hole assembly tasks,” in 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018, pp. 256–261. [Paper link]