Robot hierarchical learning method based on linear temporal logic and parallel waypoint planning

Abstract

The application discloses a kind of robot hierarchical learning method based on linear temporal logic and parallel waypoint planning, including mapping expert demonstration trajectory into action primitive sequence by inverse dynamics model, automatically dividing subtask boundary and determining waypoint quantity benchmark, while converting primitive sequence into linear temporal logic formula and giving text logical constraint;The text semantic features of logical formula are extracted in advance using pre-training encoder;Based on the current observation state, use parallel neural network architecture to synchronously predict multiple target waypoints in the whole process, eliminate time sequence dependence and solve strategy error accumulation;Based on the current state, text semantic features and target waypoints, output hybrid action instruction to drive robot to execute tasks.The application realizes the automatic decomposition from demonstration to structured task, guides policy learning through formal logical constraint, and significantly improves the robustness and sample efficiency of long-time sequence operation through parallel waypoint planning, suitable for application in object stacking, precision assembly and other tasks.

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