tagE: Enabling an Embodied Agent to Understand Human Instructions

• URL: http://arxiv.org/abs/2310.15605v1
• Date: Tue, 24 Oct 2023 08:17:48 GMT
• Title: tagE: Enabling an Embodied Agent to Understand Human Instructions
• Authors: Chayan Sarkar and Avik Mitra and Pradip Pramanick and Tapas Nayak
• Abstract summary: We introduce a novel system known as task and argument grounding for Embodied agents (tagE) At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions.
• Score: 3.943519623674811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language serves as the primary mode of communication when an intelligent agent with a physical presence engages with human beings. While a plethora of research focuses on natural language understanding (NLU), encompassing endeavors such as sentiment analysis, intent prediction, question answering, and summarization, the scope of NLU directed at situations necessitating tangible actions by an embodied agent remains limited. The inherent ambiguity and incompleteness inherent in natural language present challenges for intelligent agents striving to decipher human intention. To tackle this predicament head-on, we introduce a novel system known as task and argument grounding for Embodied agents (tagE). At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions. These extracted tasks are then mapped (or grounded) to the robot's established collection of skills, while the arguments find grounding in objects present within the environment. To facilitate the training and evaluation of our system, we have curated a dataset featuring complex instructions. The results of our experiments underscore the prowess of our approach, as it outperforms robust baseline models.

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