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.

该研究介绍了一个包含双臂任务和 / 或需要细致操纵的多样化对象操作数据集，数据集包括 224k 个剧集、双臂精细任务以及语言指令，并应用于 Dual-Action and Attention (DAA) 模型，该模型在真实机器人操作任务中展示了其在细致操纵方面的能力。