相关实验视频
Updated: May 14, 2025

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Corticospinal Excitability Modulation During Action Observation
Published on: December 31, 2013
8.8K
用户动作序列的意图推理通过对象任务和对象动作赋值的融合,基于Dempster-Shafer理论
Yaxin Liu1, Can Wang1, Yan Liu1
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新方法,用于预测轮椅上的机器人手臂的用户意图. 通过融合对象任务和行动能力,它显著减少了残疾用户的人与计算机的交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 操作轮椅安装机器人手臂 (WMRA) 对残疾人来说可能是负担.
- 现有的方法根据目光或对象选择推断用户的意图,但有局限性.
研究的目的:
- 为WMRA用户开发一种创新的意图推理方法.
- 为了减少操作负担和不必要的人机交互.
主要方法:
- 通过使用Dempster-Shafer理论 (D-S理论) 融合对象任务 (OT) 和对象动作 (OA) 能力.
- 用因果概率逻辑 (CP-Logic) 和OA赋值编码OT赋值.
- 整合OT和OA的成本,以实现动作序列推理.
主要成果:
- 与独立任务意图推断相比,拟议的方法减少了14.085%的交互.
- 与独立行动意图推断相比,它减少了52.713%的相互作用.
- 证明了更准确的用户意图捕获.
结论:
- 合并的负担能力方法显著提高了WMRA的意图推理.
- 这种方法带来了更直观,更高效的用户体验.
- 它有效地减少了人与计算机的交互,提高了残疾人的可用性.
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