{"ID":2867227,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19142","arxiv_id":"2509.19142","title":"BiGraspFormer: End-to-End Bimanual Grasp Transformer","abstract":"Bimanual grasping is essential for robots to handle large and complex objects. However, existing methods either focus solely on single-arm grasping or employ separate grasp generation and bimanual evaluation stages, leading to coordination problems including collision risks and unbalanced force distribution. To address these limitations, we propose BiGraspFormer, a unified end-to-end transformer framework that directly generates coordinated bimanual grasps from object point clouds. Our key idea is the Single-Guided Bimanual (SGB) strategy, which first generates diverse single grasp candidates using a transformer decoder, then leverages their learned features through specialized attention mechanisms to jointly predict bimanual poses and quality scores. This conditioning strategy reduces the complexity of the 12-DoF search space while ensuring coordinated bimanual manipulation. Comprehensive simulation experiments and real-world validation demonstrate that BiGraspFormer consistently outperforms existing methods while maintaining efficient inference speed (\u003c0.05s), confirming the effectiveness of our framework. Code and supplementary materials are available at https://sites.google.com/view/bigraspformer","short_abstract":"Bimanual grasping is essential for robots to handle large and complex objects. However, existing methods either focus solely on single-arm grasping or employ separate grasp generation and bimanual evaluation stages, leading to coordination problems including collision risks and unbalanced force distribution. To address...","url_abs":"https://arxiv.org/abs/2509.19142","url_pdf":"https://arxiv.org/pdf/2509.19142v2","authors":"[\"Kangmin Kim\",\"Seunghyeok Back\",\"Geonhyup Lee\",\"Sangbeom Lee\",\"Sangjun Noh\",\"Kyoobin Lee\"]","published":"2025-09-23T15:26:04Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Transformer\"]","project_urls":"[\"https://sites.google.com/view/bigraspformer\"]","has_code":false,"code_links":[{"ID":609447,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867227,"paper_url":"https://arxiv.org/abs/2509.19142","paper_title":"BiGraspFormer: End-to-End Bimanual Grasp Transformer","repo_url":"https://github.com/google/safevalues","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}