在连续环境中的限制意识零射击视觉语言导航
概括
本研究介绍了一个限制意识导航器 (CA-Nav),用于在连续环境中零射击视觉语言导航 (VLN-CE). CA-Nav通过将任务重新定义为限制意识的子指令完成,显著提高了导航成功率.
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:
- 在连续环境中视觉语言导航 (VLN-CE) 是一个挑战,特别是在零射击环境中,因为培训数据和环境结构有限.
- 现有的方法在没有专家演示的情况下,在导航稳定性和适应性方面扎.
研究的目的:
- 为零射击VLN-CE开发一种新的方法,克服当前方法的局限性.
- 在未见的环境中提高导航性能和稳定性.
主要方法:
- 建议限制意识导航器 (CA-Nav),将零射击VLN-CE重新定义为连续的,限制意识的子指令完成.
- 使用受约束感知子指令管理器 (CSM) 定义完成标准和跟踪进展.
- 使用具有超像素聚类的约束意识价值映射器 (CVM) 进行即时价值映射生成和改进.
主要成果:
- 在R2R-CE和RxR-CE基准指标上,CA-Nav实现了最先进的性能.
- 在验证成功率方面超过了以前的最佳方法12%和13%,未见的分裂.
- 在各种室内场景中在现实世界机器人部署中表现出有效性.
结论:
- CA-Nav为零射击VLN-CE提供了强大而有效的解决方案.
- 拟议的限制意识框架显著提高了复杂环境中的导航能力.
- CA-Nav显示出在自主导航领域实际应用的巨大潜力.
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