Human-in-the-loop reinforcement learning (HIL-RL) is reinforcement learning on a physical robot in which a person takes part during training: providing initial demonstrations, intervening to correct or stop the policy, and sometimes labeling success. Pure RL on hardware is usually impractical because random exploration is slow and unsafe; human input shapes exploration so that a policy can be learned in hours.
The reference system is HIL-SERL, from Luo, Xu, Wu and Levine, "Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning" (2024). It "integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices", and reports near-perfect success rates within 1 to 2.5 hours of training, with an average 2x improvement in success rate and 1.8x faster execution over imitation-learning baselines.
LeRobot v0.6.1 implements it as HIL-SERL. The workflow starts from a few teleoperated demonstrations, trains a vision-based reward model (a success classifier) on them, then runs an actor on the robot and a Soft Actor-Critic learner in parallel. The operator can take over at any time with a gamepad, keyboard or leader arm, and those interventions are recorded. Joint or end-effector bounds and image cropping keep the hardware safe and the data useful.
A common misconception is that HIL-RL is DAgger with extra steps. DAgger uses expert labels as supervised targets, so the result imitates the expert. HIL-SERL optimizes a reward, so the final policy can be faster and more reliable than behavioral cloning on the same demonstrations. It is also task-specific: each run trains one policy for one task on one setup, unlike a pretrained VLA.