Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem. In this paper we address this problem by restricting our attention to commands modeled as one action, plus arguments (also known as slots). For action detection (also called intent detection) and slot filling various architectures of Recurrent Neural Networks and Long Short Term Memory (LSTM) networks were evaluated, having LSTMs achieved a superior accuracy. As the action requested may not fall within the robots capabilities, a Support Vector Machine(SVM) is used to determine whether it is or not. For the input of the neural networks, several word embedding algorithms were compared. Finally, to implement the system in a robot, a ROS package is created using a SMACH state machine. The proposed system is then evaluated both using well-known datasets and benchmarks in the context of domestic service robots.
 A new natural language system, TINA, has been developed for applications involving spoken language tasks. TINA integrates key ideas from context free grammars, Augmented Transition Networks (ATN's), and the unification concept. TINA provides a seamless interface between syntactic and semantic analysis, and also produces a highly constraining probabilistic language model to improve recognition performance. An initial set of context-free rewrite rules provided by hand is first converted to a network structure. Probability assignments on all arcs in the network are obtained automatically from a set of example sentences. The parser uses a stack decoding search strategy, with a top-down control flow, and includes a feature-passing mechanism to deal with long-distance movement, agreement, and semantic constraints. TINA provides an automatic sentence generation capability that has been effective for identifying overgeneralization problems as well as in producing a word-pair language model for a recognizer. The parser is currently integrated with MIT's SUMMIT recognizer for use in two application domains, with the parser screening recognizer outputs either at the sentential level or to filter partial theories during the active search process. Gemini is a natural language understanding system developed for spoken language applications. This paper describes the details of the system, and includes relevant measurements of size, efficiency, and performance of each of its sub-components in detail.