{"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/egomimic-scaling-imitation-learning-via","title":"EgoMimic: Scaling Imitation Learning via Egocentric Video","arxiv_id":"2410.24221","date":"2024-10-31","proceeding":null,"authors":["Simar Kareer","Dhruv Patel","Ryan Punamiya","Pranay Mathur","Shuo Cheng","Chen Wang","Judy Hoffman","Danfei Xu"],"abstract":"The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via human embodiment data, specifically egocentric human videos paired with 3D hand tracking. EgoMimic achieves this through: (1) a system to capture human embodiment data using the ergonomic Project Aria glasses, (2) a low-cost bimanual manipulator that minimizes the kinematic gap to human data, (3) cross-domain data alignment techniques, and (4) an imitation learning architecture that co-trains on human and robot data. Compared to prior works that only extract high-level intent from human videos, our approach treats human and robot data equally as embodied demonstration data and learns a unified policy from both data sources. EgoMimic achieves significant improvement on a diverse set of long-horizon, single-arm and bimanual manipulation tasks over state-of-the-art imitation learning methods and enables generalization to entirely new scenes. Finally, we show a favorable scaling trend for EgoMimic, where adding 1 hour of additional hand data is significantly more valuable than 1 hour of additional robot data. Videos and additional information can be found at https://egomimic.github.io/","url_abs":"https://arxiv.org/abs/2410.24221v1","url_pdf":"https://arxiv.org/pdf/2410.24221v1.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":"egomimic-scaling-imitation-learning-via","repo_url":"https://github.com/SimarKareer/EgoMimic","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"}],"methods":[{"method_slug":"aria","method_name":"ARiA"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.24221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24221"}},"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/SimarKareer/EgoMimic","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"ran":1,"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":"1db8c13e93e6d733","entry":"register_algo_factory_func","repo":"SimarKareer/EgoMimic","repo_kind":"official","path":"egomimic/algo/algo.py","file_url":"https://github.com/SimarKareer/EgoMimic/blob/HEAD/egomimic/algo/algo.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1db8c13e93e6d733"}},{"code_sha256_prefix":"c9dad182621b681d","entry":"algo_factory","repo":"SimarKareer/EgoMimic","repo_kind":"official","path":"egomimic/algo/algo.py","file_url":"https://github.com/SimarKareer/EgoMimic/blob/HEAD/egomimic/algo/algo.py","link_basis":"plan_row","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":"c9dad182621b681d"}},{"code_sha256_prefix":"16f37e2539e0aca4","entry":"algo_name_to_factory_func","repo":"SimarKareer/EgoMimic","repo_kind":"official","path":"egomimic/algo/algo.py","file_url":"https://github.com/SimarKareer/EgoMimic/blob/HEAD/egomimic/algo/algo.py","link_basis":"plan_row","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":"16f37e2539e0aca4"}},{"code_sha256_prefix":"a081f9af76febfc3","entry":"config_factory","repo":"SimarKareer/EgoMimic","repo_kind":"official","path":"egomimic/configs/base_config.py","file_url":"https://github.com/SimarKareer/EgoMimic/blob/HEAD/egomimic/configs/base_config.py","link_basis":"first_harvest_node","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":"a081f9af76febfc3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}