从自然对话中实现类人任务识别
Enabling human-like task identification from natural conversation
摘要：
随着低成本复杂硬件的发展，机器人作为同事或同居者正日益成为主流。然而，可以帮助机器人硬件可用性的附带软件堆栈仍然是该过程的瓶颈，特别是如果机器人不专用于单个工作。多用途机器人的编程需要一个飞行任务调度能力，涉及任务识别和计划生成。如果机器人以自然语言接受人类的任务，则问题维度增加。虽然NLP和规划器开发的最新进展可以解决各种复杂的问题，但它们在动态机器人任务处理器中的融合在有限的范围内使用。具体地，没有详细研究从自然语言指令制定规划问题的问题。在这项工作中，我们提供了一种非平凡的方法来结合NLP引擎和规划器，使机器人可以成功地识别任务和所有相关参数，并为任务生成准确的计划。另外，需要某种机制来解决自然语言指令中的歧义或丢失的信息片段。因此，我们还开发了一种对话策略，旨在以最小的问答迭代收集更多信息，并且仅在必要时才这样做。这项工作朝着在机器人中实现类似人类的任务理解能力迈出了重要的一步。
英文摘要：
A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multi-purpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.
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。
