基于学习的机器人行为个性化,用于机器人辅助治疗
Michał Stolarz1, Alex Mitrevski1, Mohammad Wasil1
1Autonomous Systems Group, Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, Germany.
Frontiers in robotics and AI
|April 23, 2024
概括
这项研究引入了一种基于学习的行为模型,以增强治疗中的机器人自主性,减少治疗师的工作量. 政策转移显著加快学习速度,基于指导的政策显示出对用户参与和绩效的承诺.
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
- 人与计算机的交互
背景情况:
- 机器人辅助疗法通常需要显著的治疗师控制 (例如,奥兹魔法师协议),限制治疗师专注于患者互动.
- 增加机器人的自主性可以减轻治疗师的负担,并可能提高治疗会话的效率.
研究的目的:
- 开发一种基于学习的行为模型,以提高治疗中的机器人自主性.
- 研究强化学习技术,并比较用户参与和表现的奖励函数.
- 分析更容易处理的学习策略,包括用户建模,政策转移和专家反.
主要方法:
- 利用强化学习来训练机器人行为模型.
- 对比了各种奖励功能,重点关注用户参与和任务执行.
- 调查了用户群之间的政策转移,并从专家反中学习.
- 在小规模可行性研究中使用序列学习游戏评估学习模型.
主要成果:
- 政策转移显示了政策学习过程的显著加速.
- 奖励函数的选择极大地影响机器人的行动选择.
- 最初的可行性研究结果表明,从指导中学习可能会产生最佳的用户参与和游戏性能政策.
结论:
- 基于学习的方法可以在治疗环境中增强机器人的自主性.
- 政策转移是加速机器人行为学习的有效策略.
- 需要进一步的大规模研究来验证基于指导的学习对改善用户结果的有效性.
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