TY - GEN
T1 - A Natural Language Instruction Disambiguation Method for Robot Grasping
AU - Ye, Rongguang
AU - Xu, Qingchuan
AU - Liu, Jie
AU - Hong, Yang
AU - Sun, Chengfeng
AU - Chi, Wenzheng
AU - Sun, Lining
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Robot grasping under the instruction of natural language has attracted increasing attention in various applications for its advantages in enabling natural and smooth human-robot interaction. At present, mainstream algorithms mainly solve problems of utilizing simple natural language instructions to guide the robot arm to perform some specific grasping. However, for two natural language instructions with different temporal logic and the same semantics, it is usually difficult for the robot to achieve semantic disambiguation, which further leads to the failure of the grasping task. In order to address this problem, we propose a new natural language instruction disambiguation method for robot grasping by combining sentence vector similarity calculation model and sentence temporal logic model. Firstly, the word vector is obtained through the Skip-gram model in Word2vec and a sentence vector is constructed. The semantic similarity of the sentence is then calculated by using the proposed cost function. Based on the semantic similarity of the sentence, the correct temporal logic form of the sentence is then extracted according to the temporal adverbial priority to further guide the grabbing process of the robot arm. The experimental results show that our method can successfully realize the semantic disambiguation for natural language instructions with different temporal logics and the same semantics, and further guide the robot arm to complete more complicated tasks than previous tasks.
AB - Robot grasping under the instruction of natural language has attracted increasing attention in various applications for its advantages in enabling natural and smooth human-robot interaction. At present, mainstream algorithms mainly solve problems of utilizing simple natural language instructions to guide the robot arm to perform some specific grasping. However, for two natural language instructions with different temporal logic and the same semantics, it is usually difficult for the robot to achieve semantic disambiguation, which further leads to the failure of the grasping task. In order to address this problem, we propose a new natural language instruction disambiguation method for robot grasping by combining sentence vector similarity calculation model and sentence temporal logic model. Firstly, the word vector is obtained through the Skip-gram model in Word2vec and a sentence vector is constructed. The semantic similarity of the sentence is then calculated by using the proposed cost function. Based on the semantic similarity of the sentence, the correct temporal logic form of the sentence is then extracted according to the temporal adverbial priority to further guide the grabbing process of the robot arm. The experimental results show that our method can successfully realize the semantic disambiguation for natural language instructions with different temporal logics and the same semantics, and further guide the robot arm to complete more complicated tasks than previous tasks.
KW - Natural Language Instruction
KW - Robot Arm
KW - Sentence Vector Similarity Calculation
KW - Temporal Logic
UR - https://www.scopus.com/pages/publications/85128221923
U2 - 10.1109/ROBIO54168.2021.9739456
DO - 10.1109/ROBIO54168.2021.9739456
M3 - 会议稿件
AN - SCOPUS:85128221923
T3 - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
SP - 601
EP - 606
BT - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
Y2 - 27 December 2021 through 31 December 2021
ER -