The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works.


    Access

    Download


    Export, share and cite



    Title :

    Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots


    Contributors:
    Zhao, Fengda (author) / Yang, Zhikai (author) / Li, Xianshan (author) / Guo, Dingding (author) / Li, Haitao (author)

    Publication date :

    2021-03-03


    Remarks:

    doi:10.15837/ijccc.2021.2.4115
    INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol 16 No 2 (2021): International Journal of Computers Communications & Control (April) ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2021.2



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629




    Teaching robots parametrized executable plans through spoken interaction

    GEMIGNANI, GUGLIELMO / BASTIANELLI, EMANUELE / NARDI, Daniele | BASE | 2015

    Free access

    Learning procedures from interactive natural language instructions

    United States; National Aeronautics and Space Administration / United States; Air Force | British Library Conference Proceedings | 1993


    Extraction of robot primitive control rules from natural language instructions

    Wang, G. H. / Jiang, P. / Feng, Z. R. | British Library Online Contents | 2006