从不完美的人类演示中交互学习行为树
Lisa Scherf1,2, Aljoscha Schmidt1, Suman Pal3
1Interactive AI & Cognitive Models for Human-AI Interaction (IKIDA), Technische Universität Darmstadt, Darmstadt, Germany.
Frontiers in robotics and AI
|July 28, 2023
概括
这项研究引入了一个新的框架,让机器人从使用行为树 (BTs) 的人类演示中学习任务. 该系统可以互动地调整学习任务,改进机器人交互式任务学习 (ITL).
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:
- 交互式任务学习 (ITL) 使代理人能够通过人类指令来学习任务.
- 行为树 (BTs) 为任务编码提供了一个反应性和可解释的方法.
- 目前的ITL方法往往需要逐步输入或阻止无需再培训的适应.
研究的目的:
- 开发一个框架,直接从人类示范中学习行为树 (BTs).
- 使机器人能够互动地适应已学过的任务,而无需从头开始.
- 为了解决目前机器人ITL方法的局限性.
主要方法:
- 从RGB-D视频演示中学习BT.
- 从视觉特征中自动提取前后条件.
- 使用回链方法来构建反应性BT.
- 通过Web界面来互动地改进学习的BT,以解决故障情况.
主要成果:
- 从最小的人体演示中成功学习了反应性BT.
- 证明了在运行时自动检测和交互解决故障情况的能力.
- 验证了对20名参与者进行机器人垃圾处理任务的方法.
结论:
- 拟议的框架允许从视觉演示中有效地学习机器人任务.
- 交互改进显著提高了学习的BT的稳定性和适应性.
- 这种方法通过允许动态的任务适应,推动了机器人ITL领域的发展.
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