基于轻量级基础通用模型的日常环境中零射击物体检测的多步抓取框架
Ruibo Li1, Tie Zhang1, Yanbiao Zou1
1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了三步管道抓取框架 (TPGF),用于在家用机器人中高效,零射击的物体抓取. 该框架增强了日常环境中强大的机器人控制的基础模型.
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:
- 服务机器人面临的挑战是,在资源有限的家庭环境中部署对象抓取的基础模型.
- 现有的方法往往需要广泛的培训或微调,限制现实世界的适用性.
研究的目的:
- 为家庭机器人提出一个高效,零射击的物体抓取框架.
- 提高机器人控制基础模型的通用化和部署效率.
主要方法:
- 引入了三步管道掌握框架 (TPGF),包括对象感知模块 (OPM),点云提取方法 (PCEM) 与深度信息抑制 (DIS) 以及掌握姿势确定.
- 开发了EntQ-EdgeSAM,这是一个高效的模型,使用和切割来实现高精度量化,减少硬件开销.
- 将先进的基础模型集成到OPM中,以最大限度地实现零射击通用化.
主要成果:
- 在零射击物体抓获任务中,TPGF表现出强大的识别精度和高的抓获成功率.
- 和断层策略提高了3-21%的量子化准确性,并实现了95%更快的推断速度EntQ-EdgeSAM.
- 在OPM中集成的基础模型显示,与特定任务的基线相比,基本模型具有更高的概括性.
结论:
- 对于服务机器人来说,TPGF提供了一种实用且高效的解决方案,用于在日常环境中实现零射击对象抓取.
- EntQ-EdgeSAM显著提高了模型的效率,使其能够在资源有限的家庭机器人上进行实际部署.
- 该框架的零射击能力和效率对于现实世界的机器人应用非常有价值.
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