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相关实验视频

Updated: Jan 28, 2026

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LSWM:通过强化学习进行双脚运动的长短历史世界模型.

Jie Xue1,2,3, Zhiyuan Liang1,2,3, Haiming Mou3

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

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概括

这项研究引入了长短世界模型 (LSWM),以改善双脚机器人在具有挑战性的地形上运动. LSWM增强了状态重建和未来预测,显著提高了机器人的性能和适应性.

关键词:
长短 世界模型这是一个双脚机器人.国家预测模块的状态预测模块.国家重建模块.

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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
  • 控制系统 控制系统

背景情况:

  • 在非结构化的地形上,双脚机器人运动受到传感器噪音,缺失状态和未来预测不佳的限制.
  • 传统的方法依赖于国家重建的长期历史,忽视短期动态和风险预测.

研究的目的:

  • 提出和评估一个长短的世界模型 (LSWM) 来增强双脚机器人运动.
  • 为了提高对传感器噪声的稳定性,并通过使用短期历史来增强状态的可观测性.
  • 通过未来状态预测,提高适应复杂环境的能力.

主要方法:

  • 开发了一个长短世界模型 (LSWM) 框架,包含两个模块:国家重建模块 (SRM) 和未来国家预测模块 (SPM).
  • SRM利用长期历史记录来在短期历史记录中重建特权信息,提高稳定性和可观测性.
  • SPM预测未来的特权信息,以提高适应能力和风险预测.

主要成果:

  • 通过广泛的模拟和现实世界的实验,LSWM在复杂和动态环境中展示了显著的性能优势.
  • 在室内爬楼梯任务中取得了94%的成功率,超过了最先进的方法至少34%.

结论:

  • 拟议的LSWM框架有效地提高了双脚机器人在非结构化和动态环境中的移动能力.
  • 对于需要先进状态估计和预测控制的现实世界机器人应用,LSWM提供了强大的解决方案.