冲浪者:基于世界模型的视觉语言机器人操纵框架
IEEE transactions on neural networks and learning systems
|September 5, 2025
概括
通过模拟世界知识, 增强机器人的智能, 它的表现远远高于SeaWave基准值的基准值.
科学领域:
- 机器人技术
- 人工智能
- 计算机视觉
- 自然语言处理
背景情况:
- 机器人操纵在理解自然语言指令和结合世界知识方面面临挑战.
- 现有的方法往往缺乏明确的世界知识建模,并在简化模拟环境中运行.
- 嵌入式人工智能需要强大的世界模型来准确执行任务.
研究的目的:
- 介绍一个新的机器人操纵框架,
- 增强机器人操作中的新指令和场景的概括能力.
- 为评估基于视觉语言的机器人操纵提供现实的模拟和基准.
主要方法:
- 冲浪者模型将机器人操纵作为视觉场景状态转移,脱行动和场景预测.
- 该框架利用多模式信息进行增强的行动和场景预测.
- 使用MuJoCo引擎开发了一个基于物理的模拟平台,用于数据生成和测试.
主要成果:
- 冲浪者在所有操纵任务中表现出卓越的表现.
- 该框架的平均成功率为54.74%,超过了最佳基线的3.67%.
- 广泛的实验证实了Surfer在视觉语言理解和物理执行方面的有效性.
结论:
- 通过整合世界知识建模, Surfer 在机器人操纵方面取得了重大进步.
- 开发的基准和模拟器有助于对体内人工智能代理进行标准化评估.
- 该框架显示了现实应用的巨大潜力,需要细致的指令跟踪和物理交互.
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