导航到现实世界的对象
Theophile Gervet1, Soumith Chintala2, Dhruv Batra2,3
1Carnegie Mellon University, Pittsburgh, PA, USA.
Science robotics
|June 28, 2023
概括
模块化学习在移动机器人的现实世界语义导航中表现出色,成功率达到90%. 端到端的学习由于模拟到真实的差距而扎,突出了模块化.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 经典的空间导航管道缺乏对现实世界机器人部署的语义理解.
- 基于学习的方法,包括端到端和模块化方法,旨在增强机器人导航.
- 之前对视觉导航政策的评估是有限的,主要是在模拟中.
研究的目的:
- 在现实世界不受控制的环境中实证地比较语义视觉导航方法.
- 评估移动机器人的经典,模块化和端到端学习方法的性能.
- 识别机器人导航当前模拟基准中的挑战.
主要方法:
- 一项大规模的实证研究,比较语义视觉导航方法.
- 测试的方法包括经典,模块化学习和端到端学习方法.
- 在没有事先绘制地图或仪器的情况下,在六个家庭进行评估.
主要成果:
- 模块化学习在现实世界的语义导航中取得了90%的成功率.
- 端到端学习显示了显著的绩效下降,从模拟中的77%降至现实世界的23%.
- 端到端故障的主要原因被确定为模拟和现实之间的大型图像域差距.
结论:
- 模块化学习是一种可靠的方法,用于现实世界中的机器人导航到对象,从而实现有效的sim-to-real传输.
- 目前的模拟器是不可靠的基准,原因是图像和错误模式中的sim-to-real差距很大.
- 提供了改善模拟器和推进语义视觉导航研究的建议.
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