2018/05/28 by Hyemin Ahn, Sungjoon Choi, Ahn, Hyemin +7
Computer Science · Engineering · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Robotics (cs.RO) #Robotics and Automated Systems #Topic Modeling #cs.CL #cs.HC #cs.RO
paper · pdf · doi:10.48550/arxiv.1805.10799
8 pages, 9 figures
arxiv created 2018/05/28 · openalex publication_date 2018/05/28 · arxiv updated 2018/05/29 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28
In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in user's commands. We focus on the task where a robot is expected to pick up an object instructed by a human, and to interact with the human when the given instruction is vague. The proposed network understands the command from the human user and estimates the position of the desired object first. To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated. The user's answer to the question is combined with the initial command and given back to the network, resulting in more accurate estimation. The experiment results show that given unambiguous commands, the proposed method can estimate the position of the requested object with an accuracy of 98.49% based on our test dataset. Given ambiguous language commands, we show that the accuracy of the pick up task increases by 1.94 times after incorporating the information obtained from the interaction.