为6DoF机器人掌握提供生物启发的负担能力学习:基于变压器的全球特征编码方法
Zhenjie Zhao1, Hang Yu2, Hang Wu2
1College of Computer Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China; Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.
概括
这项研究介绍了一种生物灵感的神经网络用于机器人抓取,将全球场景特征与本地抓取能力相结合. 这种新的方法提高了复杂环境中的掌握成功率和清理效率.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 机器人抓取,一个关键的操纵任务,涉及识别可抓取的点和参数在3D空间 (负担能力学习) 执行.
- 当前的负担能力学习方法往往侧重于局部特征,忽视了全球场景描述因素,这些特征对于有效掌握至关重要,正如认知模型所建议的那样.
研究的目的:
- 开发一种新的生物启发的神经网络,该神经网络明确结合了全球特征编码,以改善机器人掌握.
- 通过将全球场景背景与本地特征分析相结合,增强掌握负担能力学习.
主要方法:
- 使用截断的签名距离函数 (TSDF) 作为神经网络的输入.
- 采用了变压器模型来直接编码全球场景特征.
- 通过跳过连接模块集成全球和本地特征,通过解卷模块将低层全球特征与高层本地特征合并.
主要成果:
- 在一个具有挑战性的掌握数据集上,与最先进的本地特征学习方法相比,实现了大约5%的更高的成功率和清理率.
- 在清理任务方面表现出优越的运行时间和概括能力.
- 通过在Franka Panda机器人手臂上进行现实部署来验证模型的有效性,显示与模拟数据的强烈对齐.
结论:
- 拟议的生物启发的神经网络有效地整合了全球和本地特征,以获得卓越的机器人抓取性能.
- 该方法显示了对现实世界应用程序的巨大潜力,这些应用程序需要强大的和可泛化的机器人操纵.
- 该方法通过将认知见解纳入掌握策略,推进了负担能力学习.
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