{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/163018"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/163018","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Minimalist Approach to End-to-End Vision Language Navigation with Multi-Modal Foundation Model Features","abstract":"Recent vision-language navigation (VLN) approaches leverage large models, prompt engineering, and/or explicit reasoning for instruction interpretation and agent guidance. We introduce MiniNav, a minimalist framework employing frozen vision-language foundation models as patch-wise feature extractors, avoiding data and compute heavy fine-tuning and cumbersome language model reasoning. Our lightweight control policies (∼ 10⁵ trainable parameters) are trained on a compact dataset of language-based specified navigational behaviors (∼ 10² runs, ∼ 10⁴ frames per behavior). We demonstrate generalization to novel objects and scenes, including direct real-world transfer, despite training on only two objects in a single simulated environment. Through its simple and scalable design, MiniNav provides an alternative to computationally intensive pipelines for robust real-world instruction-following. Our solution can provide a reference for evaluating the effective edge of more complex and larger VLN policies.","abstract_html":"Recent vision-language navigation (VLN) approaches leverage large models, prompt engineering, and/or explicit reasoning for instruction interpretation and agent guidance. We introduce MiniNav, a minimalist framework employing frozen vision-language foundation models as patch-wise feature extractors, avoiding data and compute heavy fine-tuning and cumbersome language model reasoning. Our lightweight control policies (∼ 10⁵ trainable parameters) are trained on a compact dataset of language-based specified navigational behaviors (∼ 10² runs, ∼ 10⁴ frames per behavior). We demonstrate generalization to novel objects and scenes, including direct real-world transfer, despite training on only two objects in a single simulated environment. Through its simple and scalable design, MiniNav provides an alternative to computationally intensive pipelines for robust real-world instruction-following. Our solution can provide a reference for evaluating the effective edge of more complex and larger VLN policies.","abstract_has_math":false,"creators":["Mishra, Kartikesh"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Rus, Daniela"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:21:13Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/163018","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rus, Daniela"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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We introduce MiniNav, a minimalist framework employing frozen vision-language foundation models as patch-wise feature extractors, avoiding data and compute heavy fine-tuning and cumbersome language model reasoning. Our lightweight control policies (∼ 10⁵ trainable parameters) are trained on a compact dataset of language-based specified navigational behaviors (∼ 10² runs, ∼ 10⁴ frames per behavior). We demonstrate generalization to novel objects and scenes, including direct real-world transfer, despite training on only two objects in a single simulated environment. Through its simple and scalable design, MiniNav provides an alternative to computationally intensive pipelines for robust real-world instruction-following. Our solution can provide a reference for evaluating the effective edge of more complex and larger VLN policies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Minimalist Approach to End-to-End Vision Language Navigation with Multi-Modal Foundation Model Features"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rus, Daniela"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Mishra, Kartikesh"],"dc:date.accessioned":["2025-10-06T17:39:52Z"],"dc:date.available":["2025-10-06T17:39:52Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Recent vision-language navigation (VLN) approaches leverage large models, prompt engineering, and/or explicit reasoning for instruction interpretation and agent guidance. We introduce MiniNav, a minimalist framework employing frozen vision-language foundation models as patch-wise feature extractors, avoiding data and compute heavy fine-tuning and cumbersome language model reasoning. Our lightweight control policies (∼ 10⁵ trainable parameters) are trained on a compact dataset of language-based specified navigational behaviors (∼ 10² runs, ∼ 10⁴ frames per behavior). We demonstrate generalization to novel objects and scenes, including direct real-world transfer, despite training on only two objects in a single simulated environment. Through its simple and scalable design, MiniNav provides an alternative to computationally intensive pipelines for robust real-world instruction-following. Our solution can provide a reference for evaluating the effective edge of more complex and larger VLN policies."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/163018"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Minimalist Approach to End-to-End Vision Language Navigation with Multi-Modal Foundation Model Features"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:13Z"}