Search Results for author:
Found 6 papers, 2 papers with code
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
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 camera.
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans.
Safe Deep Policy Adaptation
In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges.
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning
This design enables efficient synthesis of shields to monitor agents in complex environments without coordination overheads.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
