Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum

Dexterous manipulation tasks usually have multiple objectives. The priorities of these objectives may vary at different phases of a manipulation task. Current methods do not consider the objective priority and its change during the task, making a robot have a hard time or even fail to learn a good policy. In this work, we develop a novel Adaptive Hierarchical Curriculum to guide the robot to learn manipulation tasks with multiple prioritized objectives. Our method determines the objective priorities during the learning process and updates the learning sequence of the objectives to adapt to the changing priorities at different phases. A smooth transition function is developed to mitigate the effects on the learning stability when updating the learning sequence. 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 outperforms the baseline methods with a 92.5% success rate in 40 tests and on average takes 36.4% less time to finish the task.