3 citations · 5 across the 5 of their papers we have counts for
5 papers
OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
Tairan He, Zhengyi Luo, Xialin He +6
We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, Omni…
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
Tairan He, Zhengyi Luo, Wenli Xiao +4
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB ca…
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning
Wenli Xiao, Yiwei Lyu, John Dolan
Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do not have safety guarantees during the learning and deployment phases. Although shielding wi…
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…