Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering

• URL: http://arxiv.org/abs/2405.04732v3
• Date: Mon, 10 Mar 2025 21:12:19 GMT
• Title: Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering
• Authors: Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadhan, Dinesh Manocha,
• Abstract summary: We present and tackle the problem of Embodied Question Answering with Situational Queries (S-EQA) in a household environment.<n>Unlike prior EQA work, situational queries require the agent to correctly identify multiple object-states and reach a consensus on their states for an answer.<n>We introduce a novel Prompt-Generate-Evaluate scheme that wraps around an LLM's output to generate unique situational queries and corresponding consensus object information.
• Score: 48.43453390717167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries that directly reference target objects and properties ("What is the color of the car?"), situational queries (such as "Is the house ready for sleeptime?") are challenging as they require the agent to correctly identify multiple object-states (Doors: Closed, Lights: Off, etc.) and reach a consensus on their states for an answer. Towards this objective, we first introduce a novel Prompt-Generate-Evaluate (PGE) scheme that wraps around an LLM's output to generate unique situational queries and corresponding consensus object information. PGE is used to generate 2K datapoints in the VirtualHome simulator, which is then annotated for ground truth answers via a large scale user-study conducted on M-Turk. With a high rate of answerability (97.26%) on this study, we establish that LLMs are good at generating situational data. However, in evaluating the data using an LLM, we observe a low correlation of 46.2% with the ground truth human annotations; indicating that while LLMs are good at generating situational data, they struggle to answer them according to consensus. When asked for reasoning, we observe the LLM often goes against commonsense in justifying its answer. Finally, we utilize PGE to generate situational data in a real-world environment, exposing LLM hallucination in generating reliable object-states when a structured scene graph is unavailable. To the best of our knowledge, this is the first work to introduce EQA in the context of situational queries and also the first to present a generative approach for query creation. We aim to foster research on improving the real-world usability of embodied agents through this work.

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