Methods and systems for complex natural language task understanding for embodied agents

Abstract

The disclosure generally relates to methods and systems for complex natural language task understanding for embodied robots or agents. Conventional works on relation extraction generally find relevant triplets in a natural language phrase, but neither ground the task nor ground the arguments. The present disclosure implements a Grounded Argument and Task Extraction (GATE) technique that extracts a set of tasks and relevant arguments from the complex natural language instruction. The GATE uses an encoder-decoder neural network with nested decoding technique. The extracted tasks are mapped (grounded) to the known skill set of the robot and arguments are mapped (grounded) to objects within the environment, classifies the tokens as many times as possible which existing sequence labeling cannot do. The encoder-decoder neural network of the present disclosure extracts grounded task-argument pairs from a natural language instruction in a generative mechanism, and grounds the arguments based on object detector vocabulary.