机器人使用主动推理在动态环境中对目标指导的动作进行增量学习
Takazumi Matsumoto1, Wataru Ohata1, Jun Tani1
1Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究引入了一种新的主动推理模型,使机器人能够在不断变化的环境中适应行动. 机器人从人类辅导中学习,在不忘记旧任务的情况下提高新任务的性能.
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
- 认知科学 认知科学
背景情况:
- 机器人需要适应动态环境.
- 目前的模型在与突然的环境变化作斗争.
- 机器人学习通常需要人类指导.
研究的目的:
- 为实时机器人适应开发一个基于主动推理的模型.
- 使机器人能够从人类辅导中逐步学习.
- 为了防止机器人在持续学习过程中发生灾难性遗忘.
主要方法:
- 使用积极推断方法来实现目标指导的行动.
- 从自感-外感体验中实现增量学习.
- 结合过去的经验进行学习的心理排练.
- 机器人指导的综合人教示例.
主要成果:
- 积极推理模型表现出了优化参数的良好概括性.
- 在几次辅导示例后,机器人在新任务上表现得更好.
- 拟议的方案可以防止灾难性忘记以前学习的任务.
- 人类干预有时是必要的,突然,大环境变化.
结论:
- 带有增量学习的积极推断模型增强了机器人的适应能力.
- 机器人可以从有限的人类辅导中高效地学习新任务.
- 这种方法减轻了灾难性遗忘,使机器人能够持续学习.
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