User-guided motion planning with reinforcement learning for human-robot collaboration in smart manufacturing

In today's manufacturing system, robots are expected to perform increasingly complex manipulation tasks in collaboration with humans. However, current industrial robots are still largely preprogrammed with very little autonomy and still required to be reprogramed by robotics experts for even slightly changed tasks. Therefore, it is highly desirable that robots can adapt to certain task changes with motion planning strategies to easily work with non-robotic experts in manufacturing environments. In this paper, we propose a user-guided motion planning algorithm in combination with reinforcement learning (RL) method to enable robots automatically generate their motion plans for new tasks by learning from a few kinesthetic human demonstrations. Features of common human demonstrated tasks in a specific application environment, e.g., desk assembly or warehouse loading/ unloading are abstracted and saved in a library. The definition of semantical similarity between features in the library and features of a new task is proposed and further used to construct the reward function in RL. To achieve an adaptive motion plan facing task changes or new task requirements, features embedded in the library are mapped to appropriate task segments based on the trained motion planning policy using Q-learning. A new task can be either learned as a combination of a few features in the library or a requirement for further human demonstration if the current library is insufficient for the new task. We evaluate our approach on a 6 DOF UR5e robot on multiple tasks and scenarios and show the effectiveness of our method with respect to different scenarios.