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Our goal is to generate a policy to complete an unseen task given just asingle video demonstration of the task in a given domain. We hypothesize thatto successfully generalize to unseen complex tasks from a single videodemonstration, it is necessary to explicitly incorporate the compositionalstructure of the tasks into the model. To this end, we propose Neural TaskGraph (NTG) Networks, which use conjugate task graph as the intermediaterepresentation to modularize both the video demonstration and the derivedpolicy. We empirically show NTG achieves inter-task generalization on twocomplex tasks: Block Stacking in BulletPhysics and Object Collection inAI2-THOR. NTG improves data efficiency with visual input as well as achievestrong generalization without the need for dense hierarchical supervision. Wefurther show that similar performance trends hold when applied to real-worlddata. We show that NTG can effectively predict task structure on the JIGSAWSsurgical dataset and generalize to unseen tasks.

Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration