SKILL COMPOSITION AND SKILL TRAINING METHOD FOR THE DESIGN OF AUTONOMOUS SYSTEMS

The techniques disclosed herein enable a machine learning model to learn a termination condition of a sub-task. A sub-task is one of a number of sub-tasks that, when performed in sequence, accomplish a long-running task. A machine learning model used to perform the sub-task is augmented to also provide a termination signal. The termination signal indicates whether the sub-task's termination condition has been met. Monitoring the termination signal while performing the sub-task enables subsequent sub-tasks to seamlessly begin at the appropriate time. A termination condition may be learned from the same data used to train other model outputs. In some configurations, the model learns whether a sub-task is complete by periodically attempting subsequent sub-tasks. If a subsequent sub-task can be performed, positive reinforcement is provided for the termination condition. The termination condition may also be trained using synthetic scenarios designed to test when the termination condition has been met.