Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction

In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experiences (stored in the episodic and procedural memories) to shrink the search space by using reduced parameters bounds computed from the best optimizations realized by the robot with similar tasks of the new one (e.g. bin-picking from an homogenous heap of a similar object, based on visual similarity of objects stored in the semantic memory). As example, we have confronted the system to the constrained optimizations of 9 continuous hyperparameters for a professional software (Kamido) in industrial robotic arm bin-picking tasks, a step that is needed each time to handle correctly new object. We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations for each optimization) for every object tested (p-value=0.036).Comment: Accepted at the IEEE International Conference on Development and Learning and Epigenetic Robotics 2020 (ICDL-Epirob 2020

Symbol Emergence in Cognitive Developmental Systems: a Survey

OAPA Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems

Tartu Ülikooli toimetised. Tööd semiootika alalt. 1964-1992. 0259-4668

http://www.ester.ee/record=b1331700*es

The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study

Carminati MN, Knoeferle P. The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study. Presented at the Architectures and Mechanisms of Language and Processing (AMLaP), Riva del Garda, Italy

Sociocultural determination of linguistic complexity

Languages evolve, adapting to pressures arising from their learning and use. As these pressures may be different in different sociocultural environments, non-linguistic factors relating to the group structure of the people who speak a language may influence the features of the language itself. Identifying such factors, and the mechanisms by which they operate, would account for some of the diversity seen in the complexity of different languages. This thesis considers two key hypotheses which connect group structure to complex language features and evaluates them experimentally. Firstly, languages spoken by greater numbers of people are thought to be less morphologically complex than those employed by smaller groups. I assess two mechanisms by which group size could have such an effect: different degrees of variability in the linguistic input learners receive, and the effects of adult learning. Four experiments conclude that there is no evidence for different degrees of speaker input variability having any effect on the cross-generational transmission of complex morphology, and so no evidence for it being an explanation for the effect of population size on linguistic complexity. Three more experiments conclude that adult learning is a more likely mechanism, but that linking morphological simplification at the level of the individual to group-level characteristics of a language cannot be simply explained. Idiosyncratic simplifications of adult learners, when mixed with input from native speakers, may result in the linguistic input for subsequent learners being itself complex and variable, preventing simplified features from becoming more widespread. Native speaker accommodation, however, may be a key linking mechanism. Speakers of a more complex variant of a language simplify their language to facilitate communication with speakers of a simpler language. In doing so, they may increase the frequency of particular simplifications in the input of following learners. Secondly, esoteric communication | that carried out by smaller groups in which large amounts of information is shared and in which adult learning is absent | may provide the circumstances necessary for the generation and maintenance of more complex features. I assess this in four experiments. Without a learnability pressure, esoteric communication illustrates how complexity can be maintained, but there is generally no evidence of how smaller groups or those with greater amounts of shared information would develop comparatively more complex features. Any observable differences in the complexity of the languages of different types of groups is eliminated through repeated interaction between group members. There is, however, some indication that the languages used by larger groups may be more transparent, and so easier for adult learners to understand

Factors Affecting Construction of Science Discourse in the Context of an Extracurricular Science and Technology Project

Doing and learning science are social activities that require certain language, activities, and values. Both constitute what Gee (2005) calls Discourses. The language of learning science varies with the learning context (Lemke, 2001,1990). Science for All Americans (AAAS, 1990) and Inquiry and the National Science Education Standards (NRC, 2000) endorse inquiry science learning. In the United States, most science learning is teacher-centered; inquiry science learning is rare (NRC, 2000). This study focused on 12 high school students from two suburban high schools, their three faculty mentors, and two engineering mentors during an extracurricular robotics activity with FIRST Robotics Competition (FRC). FRC employed student-centered inquiry focus to teach science principles integrating technology. Research questions were (a) How do science teachers and their students enact Discourses as they teach and learn science? and (b) How does the pedagogical approach of a learning activity facilitate the Discourses that are enacted by students and teachers as they learn and teach science? Using Critical Discourse Analysis (CDA), the study examined participants’ language during robotic activities to determine how language used in learning science shaped the learning and vice versa. Data sources included video-recordings of participant language and semi-structured interviews with study participants. Transcribed recordings were coded initially using Gee’s (2005) linguistic Building Tasks as a priori codes. CDA was applied to code transcripts, to construct Discourses enacted by the participants, and to determine how context facilitated their enactment. Findings indicated that, for the students, FRC facilitated elements of Science Discourse. Wild About Robotics (W.A.R.) team became, through FRC, part of a community similar to scientists’ community that promoted knowledge and sound practices, disseminated information, supported research and development and encouraged interaction of its members. The public school science classroom in the U.S. is inimical to inquiry learning because of practices and policies associated with the epistemological stance that spawned the standards and/or testing movement and No Child Left Behind (Baez & Boyles, 2009). The findings of this study provided concrete ideas to accommodate the recommendations by NRC (1996) and NSES (2000) for creating contexts that might lead to inquiry science learning for meaningful student engagement

Of Cigarettes, High Heels, and Other Interesting Things 3/E

Among species, human beings seem to be a peculiar lot. Why is it, for example, that certain members of the species routinely put their survival at risk by puffing on a small stick of nicotine? Why is it that some females of the species make locomotion difficult for themselves by donning high-heel footwear? Are there hidden or unconscious reasons behind such strange behaviors that seem to be so utterly counter-instinctual, so to speak? For no manifest biological reason, humanity has always searched, and continues to search, for a purpose to its life. Is it this search that has led it to engage in such bizarre behaviors as smoking and wearing high heels? And is it the reason behind humanity’s invention of myths, art, rituals, languages, mathematics, science, and all the other truly remarkable things that set it apart from all other species? Clearly, Homo sapiens appears to be unique in the fact that many of its behaviors are shaped by forces other than the instincts. The discipline that endeavors to understand these forces is known as semiotics. Relatively unknown in comparison to, say, philosophy or psychology, semiotics probes the human condition in its own peculiar way, by unraveling the meanings of the signs that undergird not only the wearing of high-heel shoes, but also the construction of words, paintings, sculptures, and the like

The Use of Socially Assistive Robots with Autistic Children

The use of socially assistive robots (SARs) appears to facilitate learning, social and communication, and collaborative play in autistic children, though rigorous research to drive translation into everyday practice is limited. This thesis, comprised of four studies, was aimed at providing a comprehesive overview of how SARs have been used with young autistic people, to identify the factors that might encourage their future use, and to consider the scope of SAR benefit for autistic youth via secondary data analysis from a specific SAR support programme. The first chapters provide an overview of autism, theories, and models, and the available psychosocial support for autistic children and their families as per current practice. Within this, the different SARs types used in autism research are described followed by an outiline of the rationale for each study design methodology to address the aims of this thesis. Chapter 4 presents an up-to-date evidence summary of the nature of SARs research in autism reporting that robot-mediated support has predominantly been administered in autism clinics/centers with benefits in the social and communication skills of autistic children. Chapter 5 explores parents’/carers’ knowledge and preferences about the use of smartphones, iPods, tablets, virtual reality, robots or other technologies to support the specific needs/interests of autistic children offering guidance on how to extend the benefits of the systematic review findings. The online survey reported that 59% of parents/carers mostly preferred a tablet, followed by virtual reality and then robots that were the least preferred technologies due to being immersive, unrealistic or an unknown technology. To delve deeper into parent views about SARs, chapter 6 provides data from 12 individual interviews and one focus group with parents of autistic children. Parents were receptive to the use of a robot-mediated support acknowledging that the predictability, consistency and scaffolding of robots might facilitate learning in autism. Independent living skills and social and communication skills were the two domains of focus in future robot-mediated support with autistic children. Such a finding indicates that there may be scope to extent robots in the autism community. The final data analysed in chapter 7 draws on ten video recordings of autistic children exploring the effect of triadic robot-mediated support with a human therapist alongside a humanoid robot, called Kaspar, compared to a dyadic interaction with a human therapist alone on the development of children’s joint attention skills. Retrospective data analysis here showed no statistically significant difference in the joint attention skills of autistic children in the human therapist compared to the robot-mediated group nor in their skills from the first to the last session in either group. A statistically significant difference was observed on the requests for social games which improved from the first to the last session in the human therapist group. This study highlights the challenges SARs research facing to evidence demonstrable impact on everyday life skills as a driver of parent and child buy-in to this type of support. Taken together, the studies in this thesis suggest that SARs have a role in autism support, mainly in social and communication domains. Parents/carers have valid reasons for preferring other types of technology support though when asked to think about SARs, they do acknowledge ways in which robots may be advantegous. Existing data and secondary analysis reported that rigour in reporting the way that SARs may benefit skills development is needed and that life skills impact may be difficult to assess over a short-term period. To take SARs research forward, it is imperative to deepen partenships with autism stakeholders to ensure fit for purpose skills selection, measurement of impact, and take up of support to expand benefit