空间对象关系的交互式和增量学习来自人类演示
Rainer Kartmann1, Tamim Asfour1
1High Performance Humanoid Technologies Lab, Institute for Anthropomatics and Robotics, Department of Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Frontiers in robotics and AI
|June 5, 2023
概括
辅助机器人从人类示范中学习空间关系,以操纵物体. 这种方法逐步更新几何模型,使用少数例子进行高效的学习.
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
- 人与机器人的交互
背景情况:
- 机器人需要理解空间关系来执行任务.
- 从人类演示中学习这些概念对于辅助机器人至关重要.
研究的目的:
- 为空间关系的几何模型开发增量学习方法.
- 使机器人能够根据口头指令和演示来学习和执行任务.
主要方法:
- 使用圆柱形概率分布来表示空间关系.
- 采用模型更新的增量最大概率估计.
- 从几次在线演示中学习,而无需访问过去的数据.
主要成果:
- 从单个示范中成功创建空间关系模型.
- 通过新的演示来证明样本效率的增量更新.
- 验证了真正的人形机器人的方法.
结论:
- 拟议的方法使机器人能够有效地学习空间关系.
- 在复杂的操纵任务中促进人机协作.
- 提高辅助机器人的理解和执行指令的能力.
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