  Dexterous manipulation tasks usually have multiple objectives, and the
priorities of these objectives may vary at different phases of a manipulation
task. Varying priority makes a robot hardly or even failed to learn an optimal
policy with a deep reinforcement learning (DRL) method. To solve this problem,
we develop a novel Adaptive Hierarchical Reward Mechanism (AHRM) to guide the
DRL agent to learn manipulation tasks with multiple prioritized objectives. The
AHRM can determine the objective priorities during the learning process and
update the reward hierarchy to adapt to the changing objective priorities at
different phases. The proposed method is validated in a multi-objective
manipulation task with a JACO robot arm in which the robot needs to manipulate
a target with obstacles surrounded. The simulation and physical experiment
results show that the proposed method improved robot learning in task
performance and learning efficiency.
