Leveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot Flexibility
The framework combines a digital twin, reinforcement learning, and human demonstrations to create an online training system for collaborative robots that can adapt to workspace changes. It uses a hardware-in-the-loop setup where the twin updates in real time, allowing the RL agent to resume training when policies fail due to environmental shifts.