  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.
