结合注视注意力的多任务真实机器人数据用于双臂精细操作
Multi-task real-robot data with gaze attention for dual-arm fine manipulation
AI总结：
本文提出包含双臂精细操作任务的多任务真实机器人数据集，结合视觉注意力与双动作标签，并应用于DAA模型，在超7k次真实试验中验证了其精细操作能力。
摘要：
在机器人操作领域，深度模仿学习被认为是获取操作技能的一种有前景的方法。此外，从多样化的机器人数据集中学习被认为是实现通用性和适应性的一种可行方法。在这类研究中，通过学习各种任务，机器人实现了跨多种物体的通用性。然而，这种多任务机器人数据集主要集中在相对不精确的单臂任务上，并未涉及机器人在现实世界中预期执行的细粒度物体操作。本文介绍了一个包含双臂任务和/或需要精细操作任务的多样化物体操作数据集。为此，我们生成了一个包含 224k 个回合（150 小时，1,104 条语言指令）的数据集，其中包括移碗、开铅笔盒或剥香蕉等双臂精细任务，并且该数据是公开可用的。此外，该数据集包含视觉注意力信号以及双动作标签，这是一种将动作分为稳健的到达轨迹和与物体的精确交互的信号，以及用于实现稳健和精确物体操作的语言指令。我们将该数据集应用于我们的 Dual-Action and Attention (DAA)，这是一个专为细粒度双臂操作任务设计且对协变量偏移具有鲁棒性的模型。该模型在真实机器人操作任务中进行了超过 7k 次试验，证明了其在精细操作方面的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
