1 citations · 2 across the 6 of their papers we have counts for
7 papers
Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization
Xiao-Yin Liu, Guotao Li, Weiqun Wang +1
A significant challenge in exoskeleton robotics is the need to dynamically adapt control profiles to individual motion preferences, thereby ensuring both efficient and comfortable…
ExoTraj: A General Lower-limb Exoskeleton Assistance Policy for Complex Environments
Xiao-Yin Liu, Guotao Li, Long Sun +2
Adaptive torque prediction in dynamic exoskeleton scenarios requires expensive motion capture systems, which are infeasible in complex outdoor environments. Trajectory prediction h…
LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiency
Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou +1
Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, s…
A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou +2
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classi…
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
Xiao-Yin Liu, Xiao-Hu Zhou, Guotao Li +5
Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrain…
CROP: Conservative Reward for Model-based Offline Policy Optimization
Hao Li, Xiao-Hu Zhou, Shu-Hai Li +6
Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing off…