22 citations · 31 across the 7 of their papers we have counts for
8 papers
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
Kuangen Zhang, Jiahong Chen, Jing Wang +4
Recognizing human locomotion intent and activities is important for controlling the wearable robots while walking in complex environments. However, human-robot interface signals ar…
Preserving Domain Private Representation via Mutual Information Maximization
Jiahong Chen, Jing Wang, Weipeng Lin +2
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the…
Dynamic Modeling and Simulation of a Four-wheel Skid-Steer Mobile Robot using Linear Graphs
Eric McCormick, Haoxiang Lang, Clarence W. de Silva
This paper presents the application of the concepts and approaches of linear graph (LG) theory in the modeling and simulation of a 4-wheel skid-steer mobile robotic system. An LG r…
Automated Multi-domain Engineering Design through Linear Graph and Genetic Programming
Eric McCormick, Haoxiang Lang, Clarence W. de Silva
This paper proposes a methodology of integrating the Linear Graph (LG) approach with Genetic Programming (GP) for generating an automated multi-domain engineering design approach b…
How does the structure embedded in learning policy affect learning quadruped locomotion?
Kuangen Zhang, Jongwoo Lee, Zhimin Hou +3
Reinforcement learning (RL) is a popular data-driven method that has demonstrated great success in robotics. Previous works usually focus on learning an end-to-end (direct) policy…
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
Kuangen Zhang, Zhimin Hou, Clarence W. de Silva +2
Controlling a biped robot to walk stably is a challenging task considering its nonlinearity and hybrid dynamics. Reinforcement learning can address these issues by directly mapping…