HIL-RL

Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation

arXiv:2606.03949v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may include suboptimal actions that deviate from the desired task-execution path and force human intervention. Existing HIL-RL methods typically apply the consistent credit assignment principle to all transitions, uniformly propagating discounted terminal rewards through...