32 research outputs found
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Comprehension of spoken natural language is an essential component for robots
to communicate with human effectively. However, handling unconstrained spoken
instructions is challenging due to (1) complex structures including a wide
variety of expressions used in spoken language and (2) inherent ambiguity in
interpretation of human instructions. In this paper, we propose the first
comprehensive system that can handle unconstrained spoken language and is able
to effectively resolve ambiguity in spoken instructions. Specifically, we
integrate deep-learning-based object detection together with natural language
processing technologies to handle unconstrained spoken instructions, and
propose a method for robots to resolve instruction ambiguity through dialogue.
Through our experiments on both a simulated environment as well as a physical
industrial robot arm, we demonstrate the ability of our system to understand
natural instructions from human operators effectively, and how higher success
rates of the object picking task can be achieved through an interactive
clarification process.Comment: 9 pages. International Conference on Robotics and Automation (ICRA)
2018. Accompanying videos are available at the following links:
https://youtu.be/_Uyv1XIUqhk (the system submitted to ICRA-2018) and
http://youtu.be/DGJazkyw0Ws (with improvements after ICRA-2018 submission
コトナル タイプ ノ ドキュメント ニ タイスル チョシャ スイテイ
http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100034076&oldid=61506修士 (Master)修第1819
トウケイテキ タンゴ ブンカツ ノ ブンヤ テキオウ シュホウ
博士(Doctor)工学(Engineering)奈良先端科学技術大学院大学博第831号甲第831号博士(工学)奈良先端科学技術大学院大