{"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/open6dor-benchmarking-open-instruction-6-dof","title":"Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based Approach","arxiv_id":null,"date":"2024-10-24","proceeding":"IROS2024 2024 10","authors":["Yufei Ding","Haoran Geng","Chaoyi Xu","Xiaomeng Fang","Jiazhao Zhang","Songlin Wei","Qiyu Dai","Zhizheng Zhang","He Wang"],"abstract":"In this work, we propel the pioneer construction of the benchmark and approach for table-top Open-instruction 6-DoF Object Rearrangement (Open6DOR). Specifically, we collect a synthetic dataset of 200+ objects and carefully design 2400+ Open6DOR tasks. These tasks are divided into the Position-track, Rotation-track, and 6-DoF-track for evaluating different embodied agents in predicting the positions and rotations of target objects. Besides, we also propose a VLM-based approach for Open6DOR, named Open6DOR-GPT, which empowers GPT-4V with 3D-awareness and simulation-assistance while exploiting its strengths in generalizability and instruction-following for this task. We compare the existing embodied agents with our Open6DOR-GPT on the proposed Open6DOR benchmark and find that Open6DOR-GPT achieves the state-of-the-art performance. We further show the impressive performance of Open6DOR-GPT in diverse real-world experiments. We plan to release the final version of the benchmark, along with our refined method, in early September, and we recommend waiting until then to download the dataset.","url_abs":"https://pku-epic.github.io/Open6DOR/","url_pdf":"https://openreview.net/pdf?id=RclUiexKMt","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":"open6dor-benchmarking-open-instruction-6-dof","repo_url":"https://github.com/Selina2023/Open6DOR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"instruction-following","task_name":"Instruction Following"},{"task_slug":"object-rearrangement","task_name":"Object Rearrangement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-rearrangement-on-open6dor-v2","task":"Object Rearrangement","dataset":"Open6DOR V2","model":"Open6DOR","rank_in_archive_order":2,"of":5,"metrics":{"6-DoF":"35.6","pos-level0":"60.3","pos-level1":"78.6","rot-level0":"45.7","rot-level1":"32.5","rot-level2":"49.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}