Andrea Vanzo

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in the last years, leading to several research results and to a number of consumer products. One of the essential features of these robotic platforms is represented by the ability of interacting with users through natural language. Spoken commands can be processed by a Spoken Language Understanding chain, in order to obtain the desired behavior of the robot. The entry point of such a process is represented by an Automatic Speech Recognition (ASR) module, that provides a list of transcriptions for a given spoken utterance. Although several well-performing ASR engines are available off-the-shelf, they operate in a general purpose setting. Hence, they may be not well suited in the recognition of utterances given to robots in specific domains. In this work, we propose a practical yet robust strategy to re-rank lists of transcriptions. This approach improves the quality of ASR systems in situated scenarios, i.e., the transcription of robotic commands. The proposed method relies upon evidences derived by a semantic grammar with semantic actions, designed to model typical commands expressed in scenarios that are specific to human service robotics. The outcomes obtained through an experimental evaluation show that the approach is able to effectively outperform the ASR baseline, obtained by selecting the first transcription suggested by the ASR.

Spoken Language Understanding for Service Robotics in Italian

Robots operate in specific environments and the correct interpretation of linguistic interactions depends on physical, cognitive and language-dependent aspects triggered by the environment. In this work, we describe a Spoken Language Understanding chain for the semantic parsing of robotic commands, designed according to a Client/Server architecture. This work also reports a first evaluation of the proposed architecture in the automatic interpretation of commands expressed in Italian for a robot in a Service Robotics domain. The experimental results show that the proposed solution can be easily extended to other languages for a robust Spoken Language Understanding in Human-Robot Interaction.

Structured Learning for Context-aware Spoken Language Understanding of Robotic Commands

Service robots are expected to operate in specific environments, where the presence of humans plays a key role. A major feature of such robotics platforms is thus the ability to react to spoken commands. This requires the understanding of the user utterance with an accuracy able to trigger the robot reaction. Such correct interpretation of linguistic exchanges depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the empirical evaluation of an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational environment during both the learning and recognition stages. The effectiveness of such a context-sensitive command interpretation is tested against an extension of an already existing corpus of commands, that introduced explicit perceptual knowledge: this enabled deeper measures proving that more accurate disambiguation capabilities can be actually obtained.

Dialogue with Robots to Support Symbiotic Autonomy

Service Robotics is finding solutions to enable effective interaction with users. Among the several issues, the need of adapting robots to the way humans usually communicate is becoming a key and challenging task. In this context the design of robots that understand and reply in Natural Language plays a central role, especially when interactions involve untrained users. In particular, this is even more stressed in the framework of Symbiotic Autonomy, where an interaction is always required for the robot to accomplish a given task. In this article, we propose a framework to model dialogues with robotic platforms, enabling effective and natural dialogic interactions. The framework relies on well-known theories as well as on perceptually informed spoken language understanding processors, giving rise to interactions that are tightly bound to the operating scenario.

Enabling Symbiotic Autonomy in Short-Term Interactions: A User Study

The presence of robots in everyday environments is increasing day by day, and their deployment spans over various applications: industrial and working scenarios, health care assistance in public areas or at home. However, robots are not yet comparable to humans in terms of capabilities; hence, in the so-called Symbiotic Autonomy, robots and humans help each other to complete tasks. Therefore, it is interesting to identify the factors that allow to maximize human-robot collaboration, which is a new point of view with respect to the HRI literature and very much leaning toward a social behavior. In this work, we analyze a subset of such variables as possible influencing factors of humans’ Collaboration Attitude in a Symbiotic Autonomy framework, namely: Proxemics setting, Activity Context, and Gender and Height as valuable features of the users. We performed a user study that takes place in everyday environments expressed as activity contexts, such as relaxing and working ones. A statistical analysis of the collected results shows a high dependence of the Collaboration Attitude in different Proxemics settings and Gender.