Learning the skill of human bimanual grasping can extend the capabilities of robotic systems when grasping large or heavy objects. However, it requires a much larger search space for grasp points than single-hand grasping and numerous bimanual grasping annotations for network learning, making both data-driven or analytical grasping methods inefficient and insufficient. We propose a framework for bimanual grasp saliency learning that aims to predict the contact points for bimanual grasping based on existing human single-handed grasping data. We learn saliency corresponding vectors through minimal bimanual contact annotations that establishes correspondences between grasp positions of both hands, capable of eliminating the need for training a large-scale bimanual grasp dataset. The existing single-handed grasp saliency value serves as the initial value for bimanual grasp saliency, and we learn a saliency adjusted score that adds the initial value to obtain the final bimanual grasp saliency value, capable of predicting preferred bimanual grasp positions from single-handed grasp saliency. We also introduce a physics-balance loss function and a physics-aware refinement module that enables physical grasp balance, capable of enhancing the generalization of unknown objects. Comprehensive experiments in simulation and comparisons on dexterous grippers have demonstrated that our method can achieve balanced bimanual grasping effectively.

翻译：学习人类双手抓取技能可扩展机器人系统抓取大或重物体的能力。然而，双手抓取所需搜索的抓取点空间远超单手抓取，且神经网络学习需要大量双手抓取标注，导致数据驱动或解析式抓取方法效率低下且不充分。我们提出一种基于现有单手抓取数据预测双手抓取接触点的框架。通过少量双手接触标注学习显著性对应向量，建立双手抓取位置间的对应关系，从而无需训练大规模双手抓取数据集。以现有单手抓取显著性值作为双手抓取显著性的初始值，通过学习显著性调整分数与初始值相加得到最终双手抓取显著性值，进而从单手抓取显著性预测优选双手抓取位置。同时引入物理平衡损失函数及物理感知精化模块，实现抓取物理平衡并增强对未知物体的泛化能力。在仿真环境中的综合实验及灵巧夹爪上的对比表明，该方法能有效实现双手平衡抓取。