140 citations · 257 across the 12 of their papers we have counts for
12 papers
Height Control and Optimal Torque Planning for Jumping With Wheeled-Bipedal Robots
Yulun Zhuang, Yuan Xu, Binxin Huang +5
This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of…
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context
Jing Wang, Wonho Bae, Jiahong Chen +3
Source-free domain adaptation (SFDA) involves adapting a model originally trained using a labeled dataset ({\em source domain}) to perform effectively on an unlabeled dataset ({\em…
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…
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…
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
Zhimin Hou, Kuangen Zhang, Yi Wan +3
The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be signi…
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…
Sequential Decision Fusion for Environmental Classification in Assistive Walking
Kuangen Zhang, Wen Zhang, Wentao Xiao +3
Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. Prostheses need to understand the mot…
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Kuangen Zhang, Ming Hao, Jing Wang +2
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point c…
Sensor Fusion for Predictive Control of Human-Prosthesis-Environment Dynamics in Assistive Walking: A Survey
Kuangen Zhang, Clarence W. de Silva, Chenglong Fu
This survey paper concerns Sensor Fusion for Predictive Control of Human-Prosthesis-Environment Dynamics in Assistive Walking. The powered lower limb prosthesis can imitate the hum…
Directional PointNet: 3D Environmental Classification for Wearable Robotics
Kuangen Zhang, Jing Wang, Chenglong Fu
Environmental information can provide reliable prior information about human motion intent, which can aid the subject with wearable robotics to walk in complex environments. Previo…
Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition
Jing Wang, Kuangen Zhang
Detection and recognition of staircase as upstairs, downstairs and negative (e.g., ladder) are the fundamental of assisting the visually impaired to travel independently in unfamil…