Use of Action Label in Deep Predictive Learning for Robot Manipulation

Various forms of human knowledge can be explicitly used to enhance deep robot learning from demonstrations. Annotation of subtasks from task segmentation is one type of human symbolism and knowledge. Annotated subtasks can be referred to as action labels, which are more primitive symbols that can be building blocks for more complex human reasoning, like language instructions. However, action labels are not widely used to boost learning processes because of problems that include (1) real-time annotation for online manipulation, (2) temporal inconsistency by annotators, (3) difference in data characteristics of motor commands and action labels, and (4) annotation cost. To address these problems, we propose the Gated Action Motor Predictive Learning (GAMPL) framework to leverage action labels for improved performance. GAMPL has two modules to obtain soft action labels compatible with motor commands and to generate motion. In this study, GAMPL is evaluated for towel-folding manipulation tasks in a real environment with a six degrees-of-freedom (6 DoF) robot and shows improved generalizability with action labels.