{"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/learning-agent-aware-affordances-for-closed","title":"Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects","arxiv_id":"2209.05802","date":"2022-09-13","proceeding":null,"authors":["Giulio Schiavi","Paula Wulkop","Giuseppe Rizzi","Lionel Ott","Roland Siegwart","Jen Jen Chung"],"abstract":"Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of agent-aware affordances which fully reflect the agent's capabilities and embodiment and we show that they outperform their state-of-the-art counterparts which are only conditioned on the end-effector geometry. Additionally, closed-loop affordance inference is found to allow the agent to divide a task into multiple non-continuous motions and recover from failure and unexpected states. Finally, the pipeline is able to perform long-horizon mobile manipulation tasks, i.e. opening and closing an oven, in the real world with high success rates (opening: 71%, closing: 72%).","url_abs":"https://arxiv.org/abs/2209.05802v3","url_pdf":"https://arxiv.org/pdf/2209.05802v3.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":"learning-agent-aware-affordances-for-closed","repo_url":"https://github.com/giuschio/agent_aware_affordances","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.05802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05802"}},"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/giuschio/agent_aware_affordances","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":"e8bd5aedfb53675b","entry":"get_joint_types","repo":"giuschio/agent_aware_affordances","repo_kind":"official","path":"src/executables/0_partnet_preprocessing.py","file_url":"https://github.com/giuschio/agent_aware_affordances/blob/HEAD/src/executables/0_partnet_preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e8bd5aedfb53675b"}},{"code_sha256_prefix":"18398936809a23da","entry":"load_txt","repo":"giuschio/agent_aware_affordances","repo_kind":"official","path":"src/executables/0_partnet_preprocessing.py","file_url":"https://github.com/giuschio/agent_aware_affordances/blob/HEAD/src/executables/0_partnet_preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"18398936809a23da"}},{"code_sha256_prefix":"cdcf4a2770469149","entry":"urdf_count_movable_joints","repo":"giuschio/agent_aware_affordances","repo_kind":"official","path":"src/executables/0_partnet_preprocessing.py","file_url":"https://github.com/giuschio/agent_aware_affordances/blob/HEAD/src/executables/0_partnet_preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cdcf4a2770469149"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}