Automatic Translation of Spanish Natural Language Commands to Control Robot Comands Based on LSTM Neural Network

Bonilla, Felix Suarez; Ugalde, Federico Ruiz

doi:10.1109/irc.2019.00026

Cited by 6 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lately, deep neural networks have been evaluated in this field [23,24]. An example is the [25] study, where a seq2seq neural network with LSTM was implemented to translate Spanish commands into RCL, using a corpus resulting in 3785 sentences after expanding seven original sentences. Another significant development is the ability of robots to understand English navigation instructions as described in [26], where a dataset of 706 natural language navigation instructions was used to achieve 56% accuracy in simulation systems and 44.83% in field systems.…”

Section: Natural Language To Interact With Robotsmentioning

confidence: 99%

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Hernández,

Ortega-Mendoza,

Villatoro-Tello

et al. 2024

Mathematics

View full text Add to dashboard Cite

Human–robot interaction is becoming increasingly common to perform useful tasks in everyday life. From the human–machine communication perspective, achieving effective interaction in natural language is one challenge. To address it, natural language processing strategies have recently been used, commonly following a supervised machine learning framework. In this context, most approaches rely on the use of linguistic resources (e.g., taggers or embeddings), including training corpora. Unfortunately, such resources are scarce for some languages in specific domains, increasing the complexity of solution approaches. Motivated by these challenges, this paper explores deep learning methods for understanding natural language commands emitted to service robots that guide their movements in low-resource scenarios, defined by the use of Spanish and Nahuatl languages, for which linguistic resources are scarcely unavailable for this specific task. Particularly, we applied natural language understanding (NLU) techniques using deep neural networks and transformers-based models. As part of the research methodology, we introduced a labeled dataset of movement commands in the mentioned languages. The results show that models based on transformers work well to recognize commands (intent classification task) and their parameters (e.g., quantities and movement units) in Spanish, achieving a performance of 98.70% (accuracy) and 96.96% (F1) for the intent classification and slot-filling tasks, respectively). In Nahuatl, the best performance obtained was 93.5% (accuracy) and 88.57% (F1) in these tasks, respectively. In general, this study shows that robot movements can be guided in natural language through machine learning models using neural models and cross-lingual transfer strategies, even in low-resource scenarios.

show abstract