Towards Robust One-shot Task Execution using Knowledge Graph Embeddings

Abstract-- Requiring multiple demonstrations of a task plan presents a burden to end-users of robots. However, robustly executing tasks plans from a single end-user demonstration is an ongoing challenge in robotics. We address the problem of one-shot task execution, in which a robot must generalize a single demonstration or prototypical example of a task plan to a new execution environment. Our experimental evaluations show that our knowledge representation makes more relevant generalizations that result in significantly higher success rates over tested baselines. The task generalization module incrementally generalizes platform, which resulted in the successful generalization of failed task plans by leveraging the learned knowledge graph to initial task plans to 38 of 50 execution environments with errors infer plan constituents (see Sec IV).