DeComplex：通过搭配机器人根据复杂自然指令进行任务规划
DeComplex: Task planning from complex natural instructions by a collocating robot
摘要：
随着我们日常环境中的机器人数量的迅速增加，如家庭，办公室，餐馆，工厂车间等，自然的人机交互机制的发展变得更加重要，因为它决定了机器人的可用性和可接受性。这种同居机器人的重要特征之一是它执行以自然语言指示的任务。然而，由于自然语言表达可能具有很大的语言变化，因此执行人类预期的任务并不是微不足道的。现有的工作假设要么单一的任务指令给机器人在一个时间或有多个独立的任务在一个指令。然而，由多个相互依赖的任务组成的复杂任务指令在文献中没有被有效地处理。在任务之间可以存在排序依赖性，即，任务必须以特定顺序执行或者可能存在执行依赖性，即，一个任务的输入参数或执行取决于另一个任务的结果。如果允许使用无约束的自然语言，那么理解复杂指令中的这种依赖性就不是微不足道的。在这项工作中，我们提出了一种方法来找到多个相互依赖的任务在自然语言指令的执行顺序。基于我们的实验，我们表明，我们的系统是非常准确的从一个复杂的指令生成一个可行的执行计划。
英文摘要：
As the number of robots in our daily surroundings like home, office, restaurants, factory floors, etc. are increasing rapidly, the development of natural human-robot interaction mechanism becomes more vital as it dictates the usability and acceptability of the robots. One of the valued features of such a cohabitant robot is that it performs tasks that are instructed in natural language. However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations. Existing works assume either single task instruction is given to the robot at a time or there are multiple independent tasks in an instruction. However, complex task instructions composed of multiple inter-dependent tasks are not handled efficiently in the literature. There can be ordering dependency among the tasks, i.e., the tasks have to be executed in a certain order or there can be execution dependency, i.e., input parameter or execution of a task depends on the outcome of another task. Understanding such dependencies in a complex instruction is not trivial if an unconstrained natural language is allowed. In this work, we propose a method to find the intended order of execution of multiple inter-dependent tasks given in natural language instruction. Based on our experiment, we show that our system is very accurate in generating a viable execution plan from a complex instruction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
