对机器人的行为进行个性化因果解释
José Galeas1, Suna Bensch2, Thomas Hellström2
1Department Tecnología Electrónica, University of Málaga, Málaga, Spain.
Frontiers in robotics and AI
|October 24, 2025
概括
机器人现在可以用一个新的框架向人们解释他们的行为. 这个系统根据用户查询和社会角色来个性化机器人行为解释,以便更好地理解.
科学领域:
- 人与机器人的交互
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 机器人越来越多地在人类环境中运作,需要清晰的沟通.
- 向非专家解释机器人的行为对于信任和接受至关重要.
- 当前的解释方法往往缺乏个性化和语言多样性.
研究的目的:
- 为机器人开发一个框架,以便对机器人的行为产生个性化,上下文意识的解释.
- 整合因果推理,社会角色和自然语言处理以生成解释.
- 为了提高用户对机器人行为解释的理解.
主要方法:
- 在因果日志中将机器人事件存储为因果对.
- 利用机器学习将自然语言查询与因果日志条目相匹配.
- 确定人类调查者的社会角色.
- 通过语言多样性的大型语言模型 (LLM) 完善初始解释.
- 根据具体的查询和社会角色量身定制解释.
主要成果:
- 拟议的框架成功地产生了个性化和事实准确的解释.
- 法学士综合提供了语言多样化的响应.
- 结合因果信息,社会角色和查询上下文的解释最受欢迎.
- 定性和定量实验都验证了该方法的有效性.
结论:
- 整合因果结构,社会角色和自然语言查询可以实现个性化的机器人行为解释.
- 通过LLM增强的解释提高了语言变异,同时保持了准确性.
- 这一框架显著提高了用户对共享环境中的机器人操作的理解.
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