通过互动学习语言和机器人的行动来发展构成性
Prasanna Vijayaraghavan1, Jeffrey Frederic Queißer1, Sergio Verduzco Flores1
1Okinawa Institute of Science and Technology, Okinawa, Japan.
Science robotics
|January 22, 2025
概括
机器人可以通过结合简单的动作来学习复杂的任务,类似于人类如何概括. 在训练过程中增加任务变化,显著提高了机器人对新语言命令和相关运动的学习.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 人类的概括依赖于组合性,将任务分解为可重复使用的部分.
- 机器人技术面临的挑战是通过关联式学习,特别是使用部分数据,通过关联式学习开发语言组合性以及感觉运动技能.
研究的目的:
- 提出一个由大脑启发的神经网络模型,用于综合的感觉运动和语言学习.
- 研究关联式学习和部分数据如何影响机器人的组成技能发展.
主要方法:
- 开发了一个神经网络,使用预测编码和主动推理 (自由能量原理) 整合视觉,自身感知和语言.
- 通过模拟机器人手臂执行各种任务来评估模型的有效性.
- 进行了废除研究,以确定视觉注意力和工作记忆等关键组件.
主要成果:
- 增加了任务组合的培训变化,显著提高了对未学过的动词名词命令的概括性.
- 在语言潜伏状态空间中,自我组织的组成结构受到感觉运动学习的重大影响.
- 视觉注意力和工作记忆被发现对于准确的视觉运动序列生成来实现语言目标至关重要.
结论:
- 拟议的模型通过集成的语言和感官运动学习,证明了机器人中增强的概括性.
- 感官运动体验在语言潜伏空间内塑造构成结构方面发挥着至关重要的作用.
- 通过多式联络的互动,研究结果提供了对构成性的发育机制的洞察.
相关概念视频
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