  Both goal-agnostic and goal-oriented tasks have practical value for robotic
grasping: goal-agnostic tasks target all objects in the workspace, while
goal-oriented tasks aim at grasping pre-assigned goal objects. However, most
current grasping methods are only better at coping with one task. In this work,
we propose a bifunctional push-grasping synergistic strategy for goal-agnostic
and goal-oriented grasping tasks. Our method integrates pushing along with
grasping to pick up all objects or pre-assigned goal objects with high action
efficiency depending on the task requirement. We introduce a bifunctional
network, which takes in visual observations and outputs dense pixel-wise maps
of Q values for pushing and grasping primitive actions, to increase the
available samples in the action space. Then we propose a hierarchical
reinforcement learning framework to coordinate the two tasks by considering the
goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce
the training difficulty of the hierarchical framework, we design a two-stage
training method to train the two types of tasks separately. We perform
pre-training of the model in simulation, and then transfer the learned model to
the real world without any additional real-world fine-tuning. Experimental
results show that the proposed approach outperforms existing methods in task
completion rate and grasp success rate with less motion number. Supplementary
material is available at https:
//github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks
