Multi-task real-robot data with gaze attention for dual-arm fine manipulation
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要