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整合历史学习和多视图注意力与机器人操纵等级特征融合

Gaoxiong Lu¹, Zeyu Yan¹, Jianing Luo¹

¹The Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.

Biomimetics (Basel, Switzerland)

|November 26, 2024

概括

通过整合历史数据和多视图注意力来改进机器人操纵,使机器人能够适应环境变化并增强复杂任务的决策能力.

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
机器学习机器学习

背景情况:

机器人操纵通常依赖于当前的观察,导致环境变化或低于最佳数据的性能差.
人类决策有效地整合了过去的经验,这是许多机器人系统缺乏的能力.

研究的目的:

通过整合历史学习和多视图注意力来提高机器人操纵性能.
提高机器人在动态环境中的适应性和强度.

主要方法:

一个时空注意力机制,结合了当前/过去的观察和历史的行动.
一个基于相互信息的多视图关注模块,专注于有价值的视角.
融合语义和纹理特征,增强任务理解和细粒度操纵.

主要成果:

拟议的方法显著提高了机器人在各种任务中的操纵能力.
在模拟 (RLBench) 和现实世界的场景中都表现出了显著的稳定性和适应性.
有效处理环境变化和低于最佳的观测.

结论:

整合历史学习和多视角注意力为先进的机器人操纵提供了一个有希望的方法.
该方法提高了机器人感知环境动态和做出明智决策的能力.
这种方法有助于实现更具适应性和能力的机器人系统来完成复杂的任务.

关键词:

层次化的视觉表示.历史信息历史信息多视图注意力注意力机器人操纵是一种机器人操纵.

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