{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/embodied-question-answering","title":"Embodied Question Answering","arxiv_id":"1711.11543","date":"2017-11-30","proceeding":"CVPR 2018 6","authors":["Abhishek Das","Samyak Datta","Georgia Gkioxari","Stefan Lee","Devi Parikh","Dhruv Batra"],"abstract":"We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where\nan agent is spawned at a random location in a 3D environment and asked a\nquestion (\"What color is the car?\"). In order to answer, the agent must first\nintelligently navigate to explore the environment, gather information through\nfirst-person (egocentric) vision, and then answer the question (\"orange\").\n  This challenging task requires a range of AI skills -- active perception,\nlanguage understanding, goal-driven navigation, commonsense reasoning, and\ngrounding of language into actions. In this work, we develop the environments,\nend-to-end-trained reinforcement learning agents, and evaluation protocols for\nEmbodiedQA.","url_abs":"http://arxiv.org/abs/1711.11543v2","url_pdf":"http://arxiv.org/pdf/1711.11543v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"embodied-question-answering","repo_url":"https://github.com/abhshkdz/House3D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"embodied-question-answering","repo_url":"https://github.com/facebookresearch/EmbodiedQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"embodied-question-answering","repo_url":"https://github.com/facebookresearch/House3D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"embodied-question-answering","repo_url":"https://github.com/jxwuyi/House3D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"embodied-question-answering","task_name":"Embodied Question Answering"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[{"slug":"eqa","name":"EQA","full_name":"Embodied Question Answering"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.11543","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}