Embodied Question Answering

2017-11-30 · CVPR 2018 6 · Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra

We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange"). This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

📄 PDF Abstract BibTeX arXiv:1711.11543

Code (4)

abhshkdz/House3D

facebookresearch/EmbodiedQA pytorch

facebookresearch/House3D

jxwuyi/House3D

Tasks

Embodied Question AnsweringNavigateQuestion Answering reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real world environment. It has attracted increasing research interests due to …

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2025-03-14 · Kaixuan Jiang, Yang Liu, Weixing Chen, Jingzhou Luo 외

Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer…