  We focus on the task of goal-oriented grasping, in which a robot is supposed
to grasp a pre-assigned goal object in clutter and needs some pre-grasp actions
such as pushes to enable stable grasps. However, sample inefficiency remains a
main challenge. In this paper, a goal-conditioned hierarchical reinforcement
learning formulation with high sample efficiency is proposed to learn a
push-grasping policy for grasping a specific object in clutter. In our work,
sample efficiency is improved by two means. First, we use a goal-conditioned
mechanism by goal relabeling to enrich the replay buffer. Second, the pushing
and grasping policies are respectively regarded as a generator and a
discriminator and the pushing policy is trained with supervision of the
grasping discriminator, thus densifying pushing rewards. To deal with the
problem of distribution mismatch caused by different training settings of two
policies, an alternating training stage is added to learn pushing and grasping
in turn. A series of experiments carried out in simulation and real world
indicate that our method can quickly learn effective pushing and grasping
policies and outperforms existing methods in task completion rate and goal
grasp success rate by less times of motion. Furthermore, we validate that our
system can also adapt to goal-agnostic conditions with better performance. Note
that our system can be transferred to the real world without any fine-tuning.
Our code is available at https://github.com/xukechun/Efficient goal-oriented
push-grasping synergy
