Behavioral Cloning (BC)

Behavioral cloning is the simplest form of imitation learning: a supervised regression problem where the policy is trained to mimic expert demonstrations by minimizing the prediction error between the policy's output and the expert's action at each observed state. BC is easy to implement and scales well with data, but suffers from distributional shift — because it never receives corrective feedback, small errors cause the robot to visit states not present in the training data, which can cascade into task failure. Techniques like DAgger (Dataset Aggregation) and GAIL were developed specifically to address BC's compounding-error problem.