将人类信息转化为机器人任务:基于人类运动的动作序列识别和机器人控制
Taichi Obinata1, Kazutomo Baba2,3, Akira Uehara3,4
1Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan.
Frontiers in robotics and AI
|July 8, 2025
概括
机器人现在可以通过学习人类行为来执行复杂的研究任务. 该系统捕获运动和任务数据,使机器人能够复制连续的程序,提高研究效率和可重复性.
科学领域:
- 机器人和人机交互的人机交互
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 可穿戴的赛博格需要可靠的电源,推动电池创新.
- 目前的研究依赖于手动,试错过程,要求研究人员大量的时间和精力.
- 研究中的可复制性受到实验程序的手动性质的挑战.
研究的目的:
- 开发一种能够执行传统上由研究人员执行的连续任务的机器人系统.
- 减少研究人员的工作量,提高试错研究的可复制性.
- 使机器人能够从人类演示中学习和执行复杂的程序.
主要方法:
- 开发了一种非接触式系统,以随着时间的推移捕获3D骨运动数据.
- 使用骨架数据和对象检测创建了一个动作序列识别模型,独立于背景.
- 人类运动和任务信息被翻译为机器人执行顺序任务.
主要成果:
- 该系统在识别人类执行的任务方面取得了很高的准确性 (95.39%编辑得分,0.951 F1@10得分).
- 在50%的试验中,机器人成功地适应了工作流程的变化.
- 在从人类演示中学习后,机器人无地执行了连续的任务.
结论:
- 拟议的系统证明了机器人学习和执行复杂研究任务的可行性.
- 这项技术可以显著减少研究环境中的手工劳动.
- 该系统提高了科学实验的可重现性和效率.
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