{"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/sample-efficient-grasp-learning-using","title":"Sample Efficient Grasp Learning Using Equivariant Models","arxiv_id":"2202.09468","date":"2022-02-18","proceeding":null,"authors":["Xupeng Zhu","Dian Wang","Ondrej Biza","Guanang Su","Robin Walters","Robert Platt"],"abstract":"In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in $\\mathrm{SE}(2)$. In this paper, we recognize that the optimal grasp function is $\\mathrm{SE}(2)$-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.","url_abs":"https://arxiv.org/abs/2202.09468v1","url_pdf":"https://arxiv.org/pdf/2202.09468v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"sample-efficient-grasp-learning-using","repo_url":"https://github.com/zxp-s-works/se2-equivariant-grasp-learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.09468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09468"}},"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/zxp-s-works/se2-equivariant-grasp-learning","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"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":"c2d55584759ca004","entry":"getCurrentObs","repo":"zxp-s-works/se2-equivariant-grasp-learning","repo_kind":"official","path":"scripts/train_robot_parallel.py","file_url":"https://github.com/zxp-s-works/se2-equivariant-grasp-learning/blob/HEAD/scripts/train_robot_parallel.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c2d55584759ca004"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}