The application belongs to the technical field of robots, and particularly relates to a
robot learning method based on agile iteration aggregation, which comprises the following steps: according to an initial expert demonstration
data set, environment state features and corresponding action labels are extracted, a
robot action strategy network is pre-trained using a behavior
cloning algorithm, and an initial action strategy network is obtained; the
robot autonomously executes a current action and detects the current action; when the action is inconsistent, an interactive device is intervened and control right is taken over, the current action of the robot is corrected, and corrected trajectory data is stored; the corrected trajectory data is processed through a
delay compensation truncation mechanism and a trajectory
standardization resampling algorithm, the processed corrected trajectory data is dynamically weighted and aggregated, and an aggregated
data set is obtained; the data is input into the initial action strategy network for training, and through iterative training, a final action strategy network is obtained; and the application completes robust training of complex operation skills of the robot with extremely low labor cost and high efficiency.