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软机器人自传感的无监督SIM-to-Real适应使用双交叉模式自编码器

Chaeree Park¹, Hyunkyu Park¹, Jung Kim¹

¹Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea.

|January 6, 2025

概括

本研究介绍了软机器人无监督域适应框架,使模拟和现实数据之间的传感器校准准确. 该方法增强了软机器人体内智能和碰撞检测能力.

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:

数据驱动的校准增强了软机器人中的自身感知.
数字模拟提供了计算效率,但受到领域差距的影响.
准确的,通用的应用受限于模拟和真实传感器数据之间的差异.

研究的目的:

提出一个无监督域适应框架,用于在软机器人中对齐异质传感器域.
通过弥合模拟与现实之间的差距,实现数据效率和通用校准.
将域调整与异常检测集成在一起,以增强机器人的感知能力.

主要方法:

开发了一种双交叉模式的自动编码器,用于在没有广泛的标签的情况下进行特征级域匹配.
集成的无监督域调整与异常检测用于碰撞检测.
将框架应用于用于形状估计和碰撞检测任务的多套软机器人.

主要成果:

在模拟和现实领域实现了精确的数字生校准.
与形状估计和碰撞检测的现有基准相比,表现出卓越的性能.
在不同任务中验证了框架的可转移性和数据效率.

结论:

无监督域适应框架为软机器人体内智能提供了一种新的方法.
该方法有效地对齐异质传感器领域,提高感知能力.
这项工作通过将模拟和现实世界的数据结合起来,为更强大,更适应的软机器人系统铺平了道路.

关键词:

碰撞检测,碰撞检测,碰撞检测.域名适应域名适应自己的感觉 (proprioception),就是感觉.软机器人软机器人软机器人软传感器软传感器

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