用统一模型直接生成双手抓取姿势，避免碰撞和受力不均。
BiGraspFormer: End-to-End Bimanual Grasp Transformer
- 基于单手候选引导的双臂协同策略，降低12自由度搜索复杂度。
- 在仿真与真实场景中均超越现有方法，推理速度低于0.05秒。
- 适合需要高效协调双手操作的机器人任务，如大件物体搬运。
双手抓取对机器人处理大型复杂物体至关重要。现有方法或仅关注单臂抓取，或采用分步生成与评估流程，导致协调问题，如碰撞风险和受力不均。为此，我们提出BiGraspFormer，一种从物体点云直接生成协同双手抓取姿态的统一端到端变换器框架。核心思想是单手引导双臂（SGB）策略：先用变换器解码器生成多样单臂抓取候选，再通过专用注意力机制融合其特征，联合预测双手姿态与质量评分。该条件策略降低12自由度搜索空间复杂度，确保双臂操作协调性。综合仿真实验与真实世界验证表明，BiGraspFormer持续优于现有方法，同时保持高效推理速度（<0.05秒），证实了框架有效性。代码与补充材料见https://sites.google.com/view/bigraspformer。
原文摘要 · Abstract (English)
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 (<0.05s), confirming the effectiveness of our framework. Code and supplementary materials are available at https://sites.google.com/view/bigraspformer
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