{"ID":2856131,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11036","arxiv_id":"2510.11036","title":"XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation","abstract":"Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, demanding retraining or optimization for each novel gripper configuration. This gripper-specific retraining paradigm is neither scalable nor practical. We propose XGrasp, a real-time gripper-aware grasp detection framework that generalizes to novel gripper configurations without additional training or optimization. To resolve data scarcity, we augment existing single-gripper datasets with multi-gripper annotations by incorporating the physical characteristics and closing trajectories of diverse grippers. Each gripper is represented as a two-channel 2D image encoding its static shape (Gripper Mask) and dynamic closing trajectory (Gripper Path). XGrasp employs a hierarchical two-stage architecture consisting of a Grasp Point Predictor (GPP) and an Angle-Width Predictor (AWP). In the AWP, contrastive learning with a quality-aware anchor builds a gripper-agnostic embedding space, enabling generalization to novel grippers without additional training. Experimental results demonstrate that XGrasp outperforms existing gripper-aware methods in both grasp success rate and inference speed across diverse gripper types. Project page: https://sites.google.com/view/xgrasp","short_abstract":"Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, demanding retraining or optimization for each novel gripper configuration. This gripper-specific retraining paradigm is neither scalable nor pra...","url_abs":"https://arxiv.org/abs/2510.11036","url_pdf":"https://arxiv.org/pdf/2510.11036v2","authors":"[\"Yeonseo Lee\",\"Jungwook Mun\",\"Hyosup Shin\",\"Guebin Hwang\",\"Junhee Nam\",\"Taeyeop Lee\",\"Sungho Jo\"]","published":"2025-10-13T06:13:25Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","project_urls":"[\"https://sites.google.com/view/xgrasp\"]","has_code":false,"code_links":[{"ID":608317,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2856131,"paper_url":"https://arxiv.org/abs/2510.11036","paper_title":"XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation","repo_url":"https://github.com/google/safevalues","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}