Language-based sensing descriptors for robot object grounding

In this work, we consider an autonomous robot that is required to understand commands given by a human through natural language. Specifically, we assume that this robot is provided with an internal representation of the environment. However, such a representation is unknown to the user. In this context, we address the problem of allowing a human to understand the robot internal representation through dialog. To this end, we introduce the concept of sensing descriptors. Such representations are used by the robot to recognize unknown object properties in the given commands and warn the user about them. Additionally, we show how these properties can be learned over time by leveraging past interactions in order to enhance the grounding capabilities of the robot

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

Natural Language Interaction to Facilitate Mental Models of Remote Robots

Increasingly complex and autonomous robots are being deployed in real-world environments with far-reaching consequences. High-stakes scenarios, such as emergency response or offshore energy platform and nuclear inspections, require robot operators to have clear mental models of what the robots can and can't do. However, operators are often not the original designers of the robots and thus, they do not necessarily have such clear mental models, especially if they are novice users. This lack of mental model clarity can slow adoption and can negatively impact human-machine teaming. We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well as facilitate the evaluation of operators' mental models.Comment: In Workshop on Mental Models of Robots at HRI 202

A Voice and Pointing Gesture Interaction System for Supporting Human Spontaneous Decisions in Autonomous Cars

Autonomous cars are expected to improve road safety, traffic and mobility. It is projected that in the next 20-30 years fully autonomous vehicles will be on the market. The advancement on the research and development of this technology will allow the disengagement of humans from the driving task, which will be responsibility of the vehicle intelligence. In this scenario new vehicle interior designs are proposed, enabling more flexible human vehicle interactions inside them. In addition, as some important stakeholders propose, control elements such as the steering wheel and accelerator and brake pedals may not be needed any longer. However, this user control disengagement is one of the main issues related with the user acceptance of this technology. Users do not seem to be comfortable with the idea of giving all the decision power to the vehicle. In addition, there can be location awareness situations where the user makes a spontaneous decision and requires some type of vehicle control. Such is the case of stopping at a particular point of interest or taking a detour in the pre-calculated autonomous route of the car. Vehicle manufacturers\u27 maintain the steering wheel as a control element, allowing the driver to take over the vehicle if needed or wanted. This causes a constraint in the previously mentioned human vehicle interaction flexibility. Thus, there is an unsolved dilemma between providing users enough control over the autonomous vehicle and route so they can make spontaneous decision, and interaction flexibility inside the car. This dissertation proposes the use of a voice and pointing gesture human vehicle interaction system to solve this dilemma. Voice and pointing gestures have been identified as natural interaction techniques to guide and command mobile robots, potentially providing the needed user control over the car. On the other hand, they can be executed anywhere inside the vehicle, enabling interaction flexibility. The objective of this dissertation is to provide a strategy to support this system. For this, a method based on pointing rays intersections for the computation of the point of interest (POI) that the user is pointing to is developed. Simulation results show that this POI computation method outperforms the traditional ray-casting based by 76.5% in cluttered environments and 36.25% in combined cluttered and non-cluttered scenarios. The whole system is developed and demonstrated using a robotics simulator framework. The simulations show how voice and pointing commands performed by the user update the predefined autonomous path, based on the recognized command semantics. In addition, a dialog feedback strategy is proposed to solve conflicting situations such as ambiguity in the POI identification. This additional step is able to solve all the previously mentioned POI computation inaccuracies. In addition, it allows the user to confirm, correct or reject the performed commands in case the system misunderstands them

Integration of a voice recognition system in a social robot

Human-Robot Interaction (HRI) 1 is one of the main fields in the study and research of robotics. Within this field, dialog systems and interaction by voice play a very important role. When speaking about human- robot natural dialog we assume that the robot has the capability to accurately recognize the utterance what the human wants to transmit verbally and even its semantic meaning, but this is not always achieved. In this paper we describe the steps and requirements that we went through in order to endow the personal social robot Maggie, developed in the University Carlos III of Madrid, with the capability of understanding the natural language spoken by any human. We have analyzed the different possibilities offered by current software/hardware alternatives by testing them in real environments. We have obtained accurate data related to the speech recognition capabilities in different environments, using the most modern audio acquisition systems and analyzing not so typical parameters as user age, sex, intonation, volume and language. Finally we propose a new model to classify recognition results as accepted and rejected, based in a second ASR opinion. This new approach takes into account the pre-calculated success rate in noise intervals for each recognition framework decreasing false positives and false negatives rate.The funds have provided by the Spanish Government through the project called `Peer to Peer Robot-Human Interaction'' (R2H), of MEC (Ministry of Science and Education), and the project “A new approach to social robotics'' (AROS), of MICINN (Ministry of Science and Innovation). The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU