UW-MRS: Leveraging a Deep Grammar for Robotic Spatial Commands

Packard, Woodley

doi:10.3115/v1/s14-2144

Cited by 4 publications

(2 citation statements)

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“…In the semantic parsing of robot-directed speech, rule-based systems and methods based on symbolic AI have been used for a long time [ Figure 1 (a)] (Fischer et al, 1996 ; Lauria et al, 2002 ; Ljunglöf, 2014 ; Packard, 2014 ; Savage et al, 2019 ). However, the development of rule-based semantic parsing systems requires significant labor, and such systems are usually not robust to noise, i.e., recognition errors are incurred.…”

Section: Introductionmentioning

confidence: 99%

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

et al. 2020

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This paper describes a new method that enables a service robot to understand spoken commands in a robust manner using off-the-shelf automatic speech recognition (ASR) systems and an encoder-decoder neural network with noise injection. In numerous instances, the understanding of spoken commands in the area of service robotics is modeled as a mapping of speech signals to a sequence of commands that can be understood and performed by a robot. In a conventional approach, speech signals are recognized, and semantic parsing is applied to infer the command sequence from the utterance. However, if errors occur during the process of speech recognition, a conventional semantic parsing method cannot be appropriately applied because most natural language processing methods do not recognize such errors. We propose the use of encoder-decoder neural networks, e.g., sequence to sequence, with noise injection. The noise is injected into phoneme sequences during the training phase of encoder-decoder neural network-based semantic parsing systems. We demonstrate that the use of neural networks with a noise injection can mitigate the negative effects of speech recognition errors in understanding robot-directed speech commands i.e., increase the performance of semantic parsing. We implemented the method and evaluated it using the commands given during a general purpose service robot (GPSR) task, such as a task applied in RoboCup@Home, which is a standard service robot competition for the testing of service robots. The results of the experiment show that the proposed method, namely, sequence to sequence with noise injection (Seq2Seq-NI), outperforms the baseline methods. In addition, Seq2Seq-NI enables a robot to understand a spoken command even when the speech recognition by an off-the-shelf ASR system contains recognition errors. Moreover, in this paper we describe an experiment conducted to evaluate the influence of the injected noise and provide a discussion of the results.

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Section: Introductionmentioning

confidence: 99%

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

et al. 2020

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“…In the remainder of this section we compare the approaches and results of the six systems. Packard (2014) achieved the best score for parsing both with and without spatial context, at 92.5% and 90.5%, respectively, using a hybrid system that combines a rule-based grammar with the Berkeley parser (Petrov et al, 2006). The rule-based component uses the English Resource Grammar, a broad coverage handwritten HPSG grammar for English.…”

Section: Systems and Resultsmentioning

confidence: 99%

SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands

Dukes

2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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SemEval-2014 Task 6 aims to advance semantic parsing research by providing a high-quality annotated dataset to compare and evaluate approaches. The task focuses on contextual parsing of robotic commands, in which the additional context of spatial scenes can be used to guide a parser to control a robot arm. Six teams submitted systems using both rule-based and statistical methods. The best performing (hybrid) system scored 92.5% and 90.5% for parsing with and without spatial context. However, the best performing statistical system scored 87.35% and 60.84% respectively, indicating that generalized understanding of commands given to a robot remains challenging, despite the fixed domain used for the task. 'Move the pyramid on the blue cube on the gray one.' Figure 1: Example scene with a contextual spatial command from the Robot Commands Treebank.

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