3 accepted papers
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online RL agents trained in imperfect simulation environments can suffer from severe sim-to-real issues. Offline RL approaches a
This paper proposes a novel Q-learning-based dual-loop force tracking control framework for robot grinding tasks in uncertain environments. A complete system state-space model is established, incorporating interaction dynamics and the desired force. By augmenting the system state, a discount cost fu
Developing controllers that generalize across diverse robot morphologies remains a significant challenge in legged locomotion. Traditional approaches either create specialized controllers for each morphology or compromise performance for generality. This paper introduces a two-stage teacher-student