通过多式传感数据增强机器人技能获取:厨房任务的新型数据集
Ruochen Ren1,2,3, Zhipeng Wang4,5,6, Chaoyun Yang1,2
1National Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai, 201109, China.
Scientific data
|March 22, 2025
概括
这项研究引入了一个新的多式联络数据集,用于人机交互,捕获各种数据,如触摸和运动. 这使得机器人能够更好地理解和学习现实世界的复杂任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 人机交互 (HRI) 中的大型语言模型 (LLM) 受到单模数据的限制.
- 集成环境,生理和物理数据对于先进的HRI至关重要.
- 目前的数据集缺乏现实世界体现AI所需的复杂性.
研究的目的:
- 开发用于HRI的新型多式联运数据收集方法.
- 为了捕捉厨房环境中人类互动的复杂性.
- 创建一个全面的数据集,用于体现人工智能和机器人的技能学习.
主要方法:
- 在动态场景中收集了20名成年人使用17种厨房工具的数据.
- 获取的多式联络数据包括触觉,电磁图像,音频,全身运动和眼睛跟踪.
- 创建了一个由680个段 (~11小时) 组成的数据集,在七种模式中有56,000个注释.
主要成果:
- 成功创建了一个丰富的,多式联络数据集的人机交互.
- 该数据集捕获了任务执行期间各种人类感官和运动数据.
- 提供了机器人技能学习中实用性和可重复性的基准.
结论:
- 这项工作弥合了现实世界多式联运数据和体现的人工智能之间的差距.
- 该数据集有助于开发更有能力,更适应性的机器人.
- 建立了机器人学习技能研究的新标准.
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