5 papers
Locomotion Generation for a Rat Robot based on Environmental Changes via Reinforcement Learning
Xinhui Shan, Yuhong Huang, Zhenshan Bing +4
This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimen…
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Yu Zhang, Long Wen, Xiangtong Yao +4
This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consi…
Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments
Yu Zhang, Guangyao Tian, Long Wen +5
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static…
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…
Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor
Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…