{"url":"/sota/visual-navigation-on-ai2-thor","task":{"name":"Visual Navigation","url":"/task/visual-navigation","note":null},"dataset":{"name":"AI2-THOR","url":"/dataset/ai2-thor"},"category":"Computer Vision","categories":["Computer Vision","Robots"],"category_note":null,"description":"**Visual Navigation** is the problem of navigating an agent, e.g. a mobile robot, in an environment using camera input only. The agent is given a target image (an image it will see from the target position), and its goal is to move from its current position to the target by applying a sequence of actions, based on the camera observations only.\n\n\n<span class=\"description-source\">Source: [Vision-based Navigation Using Deep Reinforcement Learning ](https://arxiv.org/abs/1908.03627)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["SPL (All)","SPL (L≥5)","Success Rate (All)","Success Rate (L≥5)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"SPL (All)":null,"SPL (L≥5)":null,"Success Rate (All)":"higher","Success Rate (L≥5)":"higher"}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MVV-IN","metrics":{"SPL (All)":"17.27","SPL (L≥5)":"13.63","Success Rate (All)":"48.7","Success Rate (L≥5)":"30.9"},"uses_additional_data":false,"paper_date":"2020-09-01","paper":"/paper/multimodal-aggregation-approach-for-memory","paper_url":"https://arxiv.org/abs/2009.00402v1","paper_title":"Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"SAVN","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,"paper_date":"2018-12-03","paper":"/paper/learning-to-learn-how-to-learn-self-adaptive","paper_url":"http://arxiv.org/abs/1812.00971v2","paper_title":"Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning","code":"https://github.com/allenai/savn","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}