DreamerNav:使用世界模型在动态室内环境中基于学习的自主导航
Stuart Shanks1, Jonathan Embley-Riches1, Jianheng Liu1
1Robot Perception and Learning Lab, Intelligent Robotics Research Line, Department of Computer Science, University College London, London, United Kingdom.
Frontiers in robotics and AI
|October 13, 2025
概括
DreamerNav在复杂的室内环境中增强了机器人导航. 该框架使用世界模型和多模式感知来进行自适应,无碰撞的路径规划,在不同机器人平台上展示强大的性能.
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
- 强化学习是一种强化学习.
背景情况:
- 在动态的室内环境中,自主导航是一个重大挑战.
- 在部分可观测性和不确定的障碍运动下实时决策对机器人代理至关重要.
研究的目的:
- 为了介绍DreamerNav,一个机器人不可知导航框架.
- 增强先进的强化学习算法与多式联络感知和规划策略,以改善导航.
主要方法:
- 制定导航作为一个部分可观测的马尔科夫决策过程 (POMDP).
- 集成自我中心的深度图像与结构化的局部占用地图,使用循环状态空间模型 (RSSM).
- 在高保真模拟 (NVIDIA Isaac Sim) 中采用基于课程的培训.
主要成果:
- 与NoMaD,ViNT和A*相比,DreamerNav在动态环境中表现出更高的成功率和适应性.
- 该框架在混乱和动态场景中实现了无碰撞路径规划.
- 在四足机器人上的现实世界试验证实了框架的稳定性和平台独立性,而不需要重新培训.
结论:
- DreamerNav提供了一个强大的,适应性的解决方案,用于在具有挑战性的室内环境中实现自主导航.
- 多式联运感知和混合规划方法显著提高了导航性能.
- 机器人不可知性和成功的现实世界验证凸显了该框架的实际应用性.
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