  In the field of robotic manipulation, deep imitation learning is recognized
as a promising approach for acquiring manipulation skills. Additionally,
learning from diverse robot datasets is considered a viable method to achieve
versatility and adaptability. In such research, by learning various tasks,
robots achieved generality across multiple objects. However, such multi-task
robot datasets have mainly focused on single-arm tasks that are relatively
imprecise, not addressing the fine-grained object manipulation that robots are
expected to perform in the real world. This paper introduces a dataset of
diverse object manipulations that includes dual-arm tasks and/or tasks
requiring fine manipulation. To this end, we have generated dataset with 224k
episodes (150 hours, 1,104 language instructions) which includes dual-arm fine
tasks such as bowl-moving, pencil-case opening or banana-peeling, and this data
is publicly available. Additionally, this dataset includes visual attention
signals as well as dual-action labels, a signal that separates actions into a
robust reaching trajectory and precise interaction with objects, and language
instructions to achieve robust and precise object manipulation. We applied the
dataset to our Dual-Action and Attention (DAA), a model designed for
fine-grained dual arm manipulation tasks and robust against covariate shifts.
The model was tested with over 7k total trials in real robot manipulation
tasks, demonstrating its capability in fine manipulation.
