{"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/multi-object-navigation-with-dynamically","title":"Multi-Object Navigation with dynamically learned neural implicit representations","arxiv_id":"2210.05129","date":"2022-10-11","proceeding":"ICCV 2023 1","authors":["Pierre Marza","Laetitia Matignon","Olivier Simonin","Christian Wolf"],"abstract":"Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, end-to-end learning of navigation keeps some form of memory in a neural network. Networks are typically imbued with inductive biases, which can range from vectorial representations to birds-eye metric tensors or topological structures. In this work, we propose to structure neural networks with two neural implicit representations, which are learned dynamically during each episode and map the content of the scene: (i) the Semantic Finder predicts the position of a previously seen queried object; (ii) the Occupancy and Exploration Implicit Representation encapsulates information about explored area and obstacles, and is queried with a novel global read mechanism which directly maps from function space to a usable embedding space. Both representations are leveraged by an agent trained with Reinforcement Learning (RL) and learned online during each episode. We evaluate the agent on Multi-Object Navigation and show the high impact of using neural implicit representations as a memory source.","url_abs":"https://arxiv.org/abs/2210.05129v2","url_pdf":"https://arxiv.org/pdf/2210.05129v2.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":"multi-object-navigation-with-dynamically","repo_url":"https://github.com/PierreMarza/dynamic_implicit_representations","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.05129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05129"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/PierreMarza/dynamic_implicit_representations","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":6,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c0d4ae2a2ad451d8","entry":"get_embedder","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_representation.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_representation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c0d4ae2a2ad451d8"}},{"code_sha256_prefix":"62c833d50f4c5339","entry":"q_conj","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_projection.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_projection.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"62c833d50f4c5339"}},{"code_sha256_prefix":"72e903b9851c9635","entry":"q_prod","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_projection.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_projection.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"72e903b9851c9635"}},{"code_sha256_prefix":"49d17360d79ef1ca","entry":"reading_net_expl_occ_weights","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_representation_exploration_occupancy.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_representation_exploration_occupancy.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"49d17360d79ef1ca"}},{"code_sha256_prefix":"45818bdef1634521","entry":"reproject","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_projection.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_projection.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"45818bdef1634521"}},{"code_sha256_prefix":"1e45e5d13bfcf8de","entry":"run_epoch_expl_occ","repo":"PierreMarza/dynamic_implicit_representations","repo_kind":"official","path":"habitat_baselines/rl/models/implicit_representation_exploration_occupancy.py","file_url":"https://github.com/PierreMarza/dynamic_implicit_representations/blob/HEAD/habitat_baselines/rl/models/implicit_representation_exploration_occupancy.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1e45e5d13bfcf8de"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}