论文标题
交互式任务编码系统，用于学习从观察到学习
Interactive Task Encoding System for Learning-from-Observation
论文作者
论文摘要
我们介绍了交互式任务编码系统（ITE），以教机器人执行操纵任务。 ITE被设计为用于学习从观察（LFO）框架学习的输入系统，该框架可以使用少量的人类示范对家用机器人进行编程，而无需进行编码。与以前仅依靠视觉演示的LFO系统相反，ITS既利用语言指令又利用了相互作用来增强识别的鲁棒性，从而实现了多模式LFO。 ITES从口头指令中标识任务，并从视觉演示中提取参数。同时，用户审查了识别结果以进行交互式校正。在真实机器人上进行的评估证明了多种情况的多个操作的成功教学，这表明ITES对多模式LFO的有用性。源代码可从https://github.com/microsoft/symbolic-robot-teaching-interface获得。
We present the Interactive Task Encoding System (ITES) for teaching robots to perform manipulative tasks. ITES is designed as an input system for the Learning-from-Observation (LfO) framework, which enables household robots to be programmed using few-shot human demonstrations without the need for coding. In contrast to previous LfO systems that rely solely on visual demonstrations, ITES leverages both verbal instructions and interaction to enhance recognition robustness, thus enabling multimodal LfO. ITES identifies tasks from verbal instructions and extracts parameters from visual demonstrations. Meanwhile, the recognition result was reviewed by the user for interactive correction. Evaluations conducted on a real robot demonstrate the successful teaching of multiple operations for several scenarios, suggesting the usefulness of ITES for multimodal LfO. The source code is available at https://github.com/microsoft/symbolic-robot-teaching-interface.
