{"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/learning-to-learn-how-to-learn-self-adaptive","title":"Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning","arxiv_id":"1812.00971","date":"2018-12-03","proceeding":"CVPR 2019 6","authors":["Mitchell Wortsman","Kiana Ehsani","Mohammad Rastegari","Ali Farhadi","Roozbeh Mottaghi"],"abstract":"Learning is an inherently continuous phenomenon. When humans learn a new task\nthere is no explicit distinction between training and inference. As we learn a\ntask, we keep learning about it while performing the task. What we learn and\nhow we learn it varies during different stages of learning. Learning how to\nlearn and adapt is a key property that enables us to generalize effortlessly to\nnew settings. This is in contrast with conventional settings in machine\nlearning where a trained model is frozen during inference. In this paper we\nstudy the problem of learning to learn at both training and test time in the\ncontext of visual navigation. A fundamental challenge in navigation is\ngeneralization to unseen scenes. In this paper we propose a self-adaptive\nvisual navigation method (SAVN) which learns to adapt to new environments\nwithout any explicit supervision. Our solution is a meta-reinforcement learning\napproach where an agent learns a self-supervised interaction loss that\nencourages effective navigation. Our experiments, performed in the AI2-THOR\nframework, show major improvements in both success rate and SPL for visual\nnavigation in novel scenes. Our code and data are available at:\nhttps://github.com/allenai/savn .","url_abs":"http://arxiv.org/abs/1812.00971v2","url_pdf":"http://arxiv.org/pdf/1812.00971v2.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":"learning-to-learn-how-to-learn-self-adaptive","repo_url":"https://github.com/allenai/savn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"learning-to-learn-how-to-learn-self-adaptive","repo_url":"https://github.com/barmayo/spatial_attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"meta-reinforcement-learning","task_name":"Meta Reinforcement Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"visual-navigation","task_name":"Visual Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-navigation-on-ai2-thor","task":"Visual Navigation","dataset":"AI2-THOR","model":"SAVN","rank_in_archive_order":2,"of":2,"metrics":{"SPL (All)":"16.15","SPL (L≥5)":"13.91","Success Rate (All)":"40.86","Success Rate (L≥5)":"28.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.00971","atlas_url":"https://app.syntology.ai/?focus=1812.00971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00971"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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