挖掘本地和全球的时空特征,用于触觉对象识别.
Xiaoliang Qian1, Wei Deng1, Wei Wang1
1College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Frontiers in neurorobotics
|May 20, 2024
概括
这项研究引入了一个新的局部和全球残留 (LGR-18) 网络用于触摸物体识别. LGR-18网络有效地提取了本地和全球的时空特征,改善了机器人的环境感知.
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:
- 触摸物体识别 (TOR) 对机器人环境感知至关重要.
- 使用单级卷积的现有方法难以从触觉数据中提取本地和全球时空特征,从而限制了TOR的准确性.
研究的目的:
- 提出一种新的网络架构,用于增强触觉对象识别.
- 通过有效捕捉多尺度的时空特征来提高TOR的准确性.
主要方法:
- 介绍了本地和全球剩余 (LGR-18) 网络,包括多个本地和全球卷积 (LGC) 块.
- 每个LGC块集成了局部卷积 (LC) 模块 (使用时间转移和2D卷积) 和全球卷积 (GC) 模块 (1D和2D卷积的融合).
- 在LGR-18网络提取本地-全球的时空特征,而不依赖于计算上昂贵的3D卷曲.
主要成果:
- 废弃性研究证实了LC模块,GC模块和LGC块的有效性.
- 量化比较表明,在TOR任务中,与最先进的方法相比,性能优越.
结论:
- 拟议的LGR-18网络有效地提取了局部-全球的时空特征,用于触觉对象识别.
- 这种方法为3D卷曲提供了一个参数效率高的替代方案,在TOR.中实现了出色的性能.
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