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

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

A terapia de integração sensorial como facilitadora do desenvolvimento da inteligência emocional em crianças com perturbação do espectro autista e o papel das TIC

Sensory Integration Therapy (SIT) is a widely known and used intervention by a lot of professionals and practitioners in the field of special education as being an enabler of functionality of the child. Through intervention, the sensory processing components that have been affected are targeted. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is highly affected by sensory modulation disorder (SMD), a special subcategory of sensory processing disorder (SPD) that is expressed in various ways causing activation of Autonomic Nervous System (ANS) leading to biological and physiological stress. Emotional Intelligence (EI) is a complex set of skills that drive us to higher mental states. According to theorists the basis of these skills is the ability to manage and regulate stimulus. The purpose of this literature review is to investigate the connections between SIT and EI in children with ASD. In the introduction basic definitions and concepts of ASD and SIT are analyzed and in the main part correlations between EI and SMD are made, discussing the effectiveness of SIT in ASD children.La Terapia de Integración Sensorial (SIT, por sus siglas en inglés) es una intervención ampliamente conocida utilizada por muchos profesionales y practicantes en el campo de la educación especial como facilitadora del funcionamiento infantil. A través de la intervención, los componentes del procesamiento sensorial que se han visto afectados son objeto de intervención. El Trastorno del Espectro Autista (TEA) es un trastorno del neurodesarrollo que se ve muy afectado por el Trastorno de Modulación Sensorial (SMD), una subcategoría especial del Trastorno del Procesamiento Sensorial (SPD) que se expresa de varias maneras, desencadenando la activación del Sistema Nervioso Autonómico (SNA) , lo que conduce a estrés biológico y fisiológico. La Inteligencia Emocional (IE) es un conjunto complejo de habilidades que nos llevan a estados mentales superiores. Según los teóricos, la base de estas habilidades es la capacidad de gestionar y regular los estímulos. El objetivo de esta revisión de la literatura es investigar los vínculos entre SIT y EI en niños con TEA. En la introducción se analizan las definiciones y conceptos básicos de TEA y SIT y, en su mayor parte, se realizan correlaciones entre IE y SMD, discutiendo la efectividad de SIT en niños con TEA.A Terapia de Integração Sensorial (SIT) é uma intervenção amplamente conhecida e utilizada por muitos profissionais e praticantes no domínio da educação especial como facilitadora da funcionalidade da criança. Através da intervenção, os componentes do processamento sensorial que foram afetados são alvo de intervenção. A Perturbação do Espectro do Autismo (PEA) é uma perturbação do neurodesenvolvimento muito afetada pela perturbação da modulação sensorial (SMD), uma subcategoria especial da perturbação do processamento sensorial (SPD) que se exprime de várias formas, provocando a ativação do Sistema Nervoso Autónomo (SNA), o que leva a um stress biológico e fisiológico. A Inteligência Emocional (IE) é um conjunto complexo de competências que nos conduzem a estados mentais mais elevados. De acordo com os teóricos, a base destas competências é a capacidade de gerir e regular os estímulos. O objetivo desta revisão da literatura é investigar as ligações entre os SIT e a IE em crianças com PEA. Na introdução, são analisadas as definições e os conceitos básicos de PEA e de SIT e, na parte principal, são feitas correlações entre a IE e o SMD, discutindo-se a eficácia do SIT em crianças com PEA

From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.

Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities. This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd

Me and My Robot Smiled at One Another: The Process of Socially Enacted Communicative Affordance in Human-Machine Communication

The term affordance has been inconsistently applied both in robotics and communication. While the robotics perspective is mostly object-based, the communication science view is commonly user-based. In an attempt to bring the two perspectives together, this theoretical paper argues that social robots present new social communicative affordances emerging from a two-way relational process. I first explicate conceptual approaches of affordance in robotics and communication. Second, a model of enacted communicative affordance in the context of Human-Machine Communication (HMC) is presented. Third and last, I explain how a pivotal social robot characteristic—embodiment—plays a key role in the process of social communicative affordances in HMC, which may entail behavioral, emotional, and cognitive effects. The paper ends by presenting considerations for future affordance research in HMC

Final Report for the DARPA/NSF Interdisciplinary Study on Human–Robot Interaction

As part of a Defense Advanced Research Projects Agency/National Science Foundation study on human–robot interaction (HRI), over sixty representatives from academia, government, and industry participated in an interdisciplinary workshop, which allowed roboticists to interact with psychologists, sociologists, cognitive scientists, communication experts and human–computer interaction specialists to discuss common interests in the field of HRI, and to establish a dialogue across the disciplines for future collaborations. We include initial work that was done in preparation for the workshop, links to keynote and other presentations, and a summary of the findings, outcomes, and recommendations that were generated by the participants. Findings of the study include— the need for more extensive interdisciplinary interaction, identification of basic taxonomies and research issues, social informatics, establishment of a small number of common application domains, and field experience for members of the HRI community. An overall conclusion of the workshop was expressed as the following— HRI is a cross-disciplinary area, which poses barriers to meaningful research, synthesis, and technology transfer. The vocabularies, experiences, methodologies, and metrics of the communities are sufficiently different that cross-disciplinary research is unlikely to happen without sustained funding and an infrastructure to establish a new HRI community

Bayesian Inference of Self-intention Attributed by Observer

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior

AUTISTHERAPIBOT: A New Robotic Approach for Autistic Children

Recent studies unravels that there are a lot of negative implications happen in those children who suffers from Autistic Spectrum Disorder (ASD) which include Asperger and Kanner Syndrome. All these syndromes shares similar characteristics which are difficulties in socialization, communications and repetitive inflexible behaviors. This problem leads to difficulties in learning especially to those children suffering from autistic disorder. Thus, the objective of this project is to investigate the current teaching method used by the autism therapists in Perak at the selected special school and is to develop an autonomous robotic system to aid the teaching and learning process of autistic children in Malaysia. The LEGO Mindstorm NXT is used as an alternative approach in educating those children with ASD namely those in the Asperger range since Asperger is more common compared to Kanner. The prototype, is tested on a group of autistic kids from selected public and private autism institution. The project focuses on how to attract the autistic children and sustain their learning via the usage of robotic application such as the LEGO Mindstorm NXT. In a preliminary investigation, multiple robotic designs and programming approach are experimented to produce a robotic application that can engage with the target autistic children in order to facilitate their process of learning via the intervention of their therapists. Interviews with the therapists and live observation at the selected special school are conducted to understand the traditional learning process that are used by the therapists and identify the weaknesses in it to improvise it. The results from the investigation and tests shows that this robotic systems helps a lot in assisting the therapist in educating autistic children and scores way better if compared to the current methods they are using. The significance of this robotic application is to fulfill the depravedness in the learning capabilities of the autistic children and also to assist the therapists in their daily routine