What should a robot learn from an infant? Mechanisms of action interpretation and observational learning in infancy

The paper provides a summary of our recent research on preverbal infants (using violation-of-expectation and observational learning paradigms) demonstrating that one-year-olds interpret and draw systematic inferences about other’s goal-directed actions, and can rely on such inferences when imitating other’s actions or emulating their goals. To account for these findings it is proposed that one-year-olds apply a non-mentalistic action interpretational system, the ’teleological stance’ that represents actions by relating relevant aspects of reality (action, goal-state, and situational constraints) through the principle of rational action, which assumes that actions function to realize goal-states by the most efficient means available in the actor’s situation. The relevance of these research findings and the proposed theoretical model for how to realize the goal of epigenetic robotics of building a ’socially relevant’ humanoid robot is discussed

Towards a Theory Grounded Theory of Language

In this paper, we build upon the idea of theory grounding and propose one specific form of theory grounding, a theory of language. Theory grounding is the idea that we can imbue our embodied artificially intelligent systems with theories by modeling the way humans, and specifically young children, develop skills with theories. Modeling theory development promises to increase the conceptual and behavioral flexibility of these systems. An example of theory development in children is the social understanding referred to as “theory of mind.” Language is a natural task for theory grounding because it is vital in symbolic skills and apparently necessary in developing theories. Word learning, and specifically developing a concept of words, is proposed as the first step in a theory grounded theory of language

A Theory of (the Technological) Mind: Developing Understanding of Robot Minds

The purpose of this dissertation is to explore how children attribute minds to social robots and the impacts that these attributions have on children’s interactions with robots, specifically their feelings toward and willingness to trust them. These are important areas of study as robots become increasingly present in children’s lives. The research was designed to address a variety of questions regarding children’s willingness to attribute mental abilities to robots: (1) To what extent do children perceive that social robots share similarities with people and to what extent do they believe they have human-like minds? (2) Do attributions of human-like qualities to robots affect children’s ability to understand and interact with them? (3) Does this understanding influence children’s willingness to accept information from robots? And, of crucial importance, (4) how do answers to these questions vary with age? Across a series of five studies, I investigated children’s beliefs about the minds of robots, and for comparison adults’ beliefs, using survey methods and video stimuli. Children watched videos of real-life robots and in response to targeted questions reported on their beliefs about the minds of those robots, their feelings about those robots, and their willingness to trust information received from those robots. Using a variety of statistical methods (e.g., factor analysis, regression modeling, clustering methods, and linear mixed-effects modeling), I uncovered how attributions of a human-like mind impact feelings toward robots, and trust in information received from robots. Furthermore, I explored how the design of the robot and features of the child relate to attributions of mind to robots. First and foremost, I found that children are willing to attribute human-like mental abilities to robots, but these attributions decline with age. Moreover, attributions of mind are linked to feelings toward robots: Young children prefer robots that appear to have human-like minds, but this reverses with age because older children and adults do not (Chapter II). Young children are also willing to trust a previously accurate robot informant and mistrust a previously inaccurate one, much like they would with accurate and inaccurate human informants, when they believe that the robot has mental abilities related to psychological agency (Chapter III). Finally, while qualities of the robot, like behavior and appearance, are linked to attributions of mind to the robot, individual differences across children and adults are likely the primary mechanisms that explain how and when children and adults attribute mental abilities to robots (Chapter IV). That is, individuals are likely to attribute similar mental abilities to a wide variety of robots that have differing appearances and engage in a variety of different actions. These studies provide a variety of heretofore unknown findings linking the developmental attributions of minds to robots with judgments of robots’ actions, feelings about robots, and learning from robots. It remains to be seen, however, the exact nature of the mechanisms and the child-specific features that increase children’s willingness to attribute mental abilities to robots.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146010/1/kabrink_1.pd

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Creepiness Creeps In: Uncanny Valley Feelings Are Acquired in Childhood

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150519/1/cdev12999_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150519/2/cdev12999.pd

Lexical distributional cues, but not situational cues, are readily used to learn abstract locative verb-structure associations.

Children must learn the structural biases of locative verbs in order to avoid making overgeneralisation errors (e.g., *I filled water into the glass). It is thought that they use linguistic and situational information to learn verb classes that encode structural biases. In addition to situational cues, we examined whether children and adults could use the lexical distribution of nouns in the post-verbal noun phrase to assign novel verbs to locative classes. In Experiment 1, children and adults used lexical distributional cues to assign verb classes, but were unable to use situational cues appropriately. In Experiment 2, adults generalised distributionally-learned classes to novel verb arguments, demonstrating that distributional information can cue abstract verb classes. Taken together, these studies show that human language learners can use a lexical distributional mechanism that is similar to that used by computational linguistic systems that use large unlabelled corpora to learn verb meaning

Shall I trust you? From child-robot interaction to trusting relationships

Studying trust in the context of human-robot interaction is of great importance given the increasing relevance and presence of robotic agents in various social settings, from educational to clinical. In the present study, we investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children\u2019s beliefs about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children\u2019s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, as opposed to 7-years-olds, who displayed the reverse pattern. These findings align with results showing that, for children aged 3 and 7 years, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, a dichotomy was found between attribution of mental states to the human and robot and children\u2019s behavior: while attributing significantly lower mental states to the robot than the human, in the trusting game children behaved similarly when they related to the human and the robot. Altogether, the results of this study highlight that comparable psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay \u2013 during development \u2013 between children\u2019s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children\u2019s age as well as the interactive partner\u2019s nature (human vs. robot)

The man and the machine : do children learn from and transmit tool-use knowledge acquired from a robot in ways that are comparable to a human model?

Abstract: Robots are an increasingly prevalent presence in children’s lives. However, little is known about the ways in which children learn from robots and whether they do so in the same way as they learn from humans. To investigate this, we adapted a previously established imitation paradigm centered on inefficient tool use. Children (3- to 6-year-olds; N = 121) were measured on their acquisition and transmission of normative knowledge modeled by a human or a robot. Children were more likely to adopt use of a normative tool and to transmit this knowledge to another when shown how to do so by the human than when shown how to do so by the robot. Older children (5- and 6-year-olds) were less likely than younger children (3- and 4-year-olds) to select the normative tool. Our findings suggest that preschool children are capable of copying and transmitting normative techniques from both human and robot models, albeit at different rates and dependent on age