This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these (...) capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots. (shrink)

The study of imitation and other mechanisms of social learning is an exciting area of research for all those interested in understanding the origin and the nature of animal learning in a social context. Moreover, imitation is an increasingly important research topic in Artificial Intelligence and social robotics which opens up the possibility of individualized social intelligence in robots that are part of a community, and allows us to harness not only individual learning by the single robot, but also the (...) acquisition of new skills by observing other members of the community. After an introduction to the main research issues in research on imitation in various fields, we motivate the particular focus of this work, namely the correspondence problem. We describe Action Learning for Imitation via Correspondences between Embodiments, an implemented generic framework for solving the correspondence problem between differently embodied robots. Alice enables a robotic agent to learn a behavioral repertoire suitable to performing a task by observing a model agent. Importantly, the model agent could possibly possess a different type of body, e.g. a different number of limbs or joints, a different height, different sensors, a different basic action repertoire, etc. Previously, in a test-bed where the agents differed according to their possible movement patterns, we demonstrated that the character of imitation achieved will depend on the granularity of subgoal matching, and on the metrics used to evaluate success. In our current work, we implemented Alice in a new test-bed called Rabit where simple simulated robotic arm agents use various metrics for evaluating success according to actions, states, effects, or weighted combinations. We examine the roles of synchronization, looseness of perceptual matching, and of proprioceptive matching by a series of experiments. Also, we study how Alice copes with changes in the embodiment of the imitator during learning. Our simulation results suggest that synchronization and loose perceptual matching allow for faster acquisition of behavioral compentencies at low error rates. Social learning plays a role as a replication mechanism for behaviors and results in variability when the transmitted behavior differs from the model’s behavior, thus providing the evolutionary substrate for culture and its pre-cursors. Social learning in robotics could therefore serve as the basis for culture in societies whose members include artificial agents. We address the use of imitative social learning mechanisms like Alice for transmission of skills between robots, and give first examples of transmission of a skill despite differences in embodiment of agents involved. In the particular setup, transmission occurs through a chain, as well as emerging in cyclic arrangements of robots. These simple examples demonstrate that by using social learning and imitation, cultural transmission is possible among robots, even in heterogeneous groups of robots. (shrink)

Research into robotic social learning, especially that concerned with imitation, often focuses at differing ends of a spectrum from observational learning at one end to following or matched-dependent behaviour at the other. We study the implications and differences that arise when carrying out experiments both at the extremes and within this spectrum. Physical Khepera robots with minimal sensory capabilities are used, and after training, experiments are carried out where an imitating robot perceives the dynamic movement behaviours of another model robot (...) carrying a light source. It learns the movement behaviour of the model by either statically observing the model, dynamically observing the model or by following the model. It finally re-enacts the learnt behaviour. We compare the results of these re-enactments and illustrate the differences and trade-offs that arise between static observational and reactive following learning methods. We also consider circumstances where, for this robotic embodiment, dynamic observation has both advantages and disadvantages when compared to static observation. We conclude by discussing the implications that arise from using and combining these types of social learning. (shrink)

How do we perceive robots practising a task that we have taught them? While learning, human trainees usually provide nonverbal cues that reveal their level of understanding and interest in the task. Similarly, nonverbal social cues of trainee robots that can be interpreted naturally by humans can enhance robot learning. In this article, we investigated a scenario in which a robot is practising a physical task in front of the human teachers, who were asked to assume that they had previously (...) taught the robot to perform that task. Through an online experiment with 167 participants, we examined the effects of different gaze patterns and arm movements with multiple speeds and various kinds of pauses on human teachers’ perception of different attributes of the robot. We found that the perception of a trainee robot’s attributes can be systematically affected by its behaviours. Findings of this study can inform designing more successful nonverbal social interactions for intelligent robots. (shrink)

The study of imitation and other mechanisms of social learning is an exciting area of research for all those interested in understanding the origin and the nature of animal learning in asocial context. Moreover, imitation is an increasingly important research topic in Artificial Intelligence and social robotics which opens up the possibility ofindividualized social intelligencein robots that are part of a community, and allows us to harness not only individual learning by the single robot, but also the acquisition of new (...) skills by observing other members of the community. After an introduction to the main research issues in research on imitation in various fields, we motivate the particular focus of this work, namely thecorrespondence problem. We describeAction Learning for Imitation via Correspondences between Embodiments, an implemented generic framework for solving the correspondence problem between differently embodied robots. Alice enables a robotic agent to learn a behavioral repertoire suitable to performing a task by observing a model agent. Importantly, the model agent could possibly possess a different type of body, e.g. a different number of limbs or joints, a different height, different sensors, a different basic action repertoire, etc. Previously, in a test-bed where the agents differed according to their possible movement patterns, we demonstrated that the character of imitation achieved will depend on the granularity of subgoal matching, and on the metrics used to evaluate success.In our current work, we implemented Alice in a new test-bed calledRabitwhere simple simulated robotic arm agents use various metrics for evaluating success according to actions, states, effects, or weighted combinations.We examine the roles ofsynchronization,looseness of perceptual matching, and ofproprioceptive matchingby a series of experiments. Also, we study how Alice copes withchanges in the embodimentof the imitator during learning. Our simulation results suggest thatsynchronizationandloose perceptual matchingallow for faster acquisition of behavioral compentencies at low error rates.Social learning plays a role as areplicationmechanism for behaviors and results invariabilitywhen the transmitted behavior differs from the model’s behavior, thus providing theevolutionary substrate for cultureand its pre-cursors. Social learning in robotics could therefore serve as the basis for culture in societies whose members include artificial agents. We address the use of imitative social learning mechanisms like Alice for transmission of skills between robots, and give first examples of transmission of a skill despite differences in embodiment of agents involved. In the particular setup, transmission occurs through a chain, as well as emerging in cyclic arrangements of robots. These simple examples demonstrate that by using social learning and imitation,cultural transmissionis possible among robots, even in heterogeneous groups of robots. (shrink)

This paper describes our general framework for the investigation of how human gestures can be used to facilitate the interaction and communication between humans and robots. Two studies were carried out to reveal which “naturally occurring” gestures can be observed in a scenario where users had to explain to a robot how to perform a home task. Both studies followed a within-subjects design: participants had to demonstrate how to lay a table to a robot using two different methods — utilizing (...) only gestures or gestures and speech. The first study enabled the validation of the COGNIRON coding scheme for human gestures in Human–Robot Interaction. Based on the data collected in both studies, an annotated video corpus was produced and characteristics such as frequency and duration of the different gestural classes have been gathered to help capture requirements for the designers of HRI systems. The results from the first study regarding the frequencies of the gestural types suggest an interaction between the order of presentation of the two methods and the actual type of gestures produced. However, the analysis of the speech produced along with the gestures did not reveal differences due to ordering of the experimental conditions. The second study expands the issues addressed by the first study: we aimed at extending the role of the interaction partner by introducing some positive acknowledgement of the participants’ activity. The results show no significant differences in the distribution of gestures between the two explanation methods, in contrast to the previous study. Implications for HRI are discussed focusing on issues relevant for the design of the robot’s communication skills to support the interaction loop with humans in home scenarios. (shrink)