Robot task error recovery using Petri nets learned from demonstration

The ability to recover from errors is necessary for robots to cope with unexpected situations in a dynamic environment. Efficient error recovery should allow the robot to utilise existing knowledge of the task and learn new error recovery strategies from observation. This paper proposes an automatic error recovery procedure that allows the robot to handle both known and unknown error states using a Petri net representation of the task. For known error states, the robot can directly adjust the sequencing of actions using the Petri net representation to complete the task, while for unknown error states, the robot can learn how to perform error recovery from a human demonstrator by extending the existing Petri net. The proposed method is verified on a real robot performing a block stacking task.