Training-Free Task Planning by Parsing Language Signals With Common Sense

Abstract

Task planning refers to autonomously organizing actions in response to instruction signals, especially language signals. Previous reinforcement learning and imitation learning methods always require a large amount of task-related data (data interacting with the environment or expert demonstrations) for policy training. In this paper, we propose a method named Language Signal Parse Tree (LSPT for short) for task planning. LSPT is a training-free method and consists of a language signal parser and a precondition action generator. The language signal parser translates goal predicates (neural language instructions) into a predicate parse tree. This tree contains essential information about position and state changes, which is subsequently employed to generate key actions. The precondition action generator leverages commonsense to produce additional pre-actions that complement each key action, thereby increasing the likelihood of successful execution of all actions. The final plan used to accomplish the task incorporates both key actions and pre-actions. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art method LID by 24.8% on In-Distribution dataset, 43.2% on Novel Scenes dataset, and 38.8% on Novel Tasks dataset.