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We used a multilevel growth model to describe the developmental trajectories of infant’s coordinated attention between people and objects between 7 and 10 months of age. Additionally, we assed whether the coordinated attention looks were accompanied by smiles as infants interacted social partners. These results confirm the emergence of visual joint attention skills before the end of the first year. These results will be useful in the construction of robotic systems that engage in joint attention.

This paper introduces an educational software platform for a small teddy-bear-like robot, RobotPHONE. Utilizing back-drivability of the three joints (totally 6 DOFs) of the robot, the platform enables the robot to learn correspondences between gestures (posed by a human teacher) and voice (given by a human). Because the motion and the voice can be symmetrically produced and recognized, the robot would be an ideal tool for the research and education in learning and development.

Children with autism have difficulties in social interaction with other people and much attention in recent years has been directed to robots as therapy tools. We studied the social interaction between children with autism and robots longitudinally to observe developmental changes in their performance. We observed children at a special school for six months and analyzed their performance with robots. The results showed that two children adapted to the experimental situations and developed interaction with the robots. This suggests that they changed their interaction with the robots from an object-like one into an agentlike one.

We describe sensorimotor phase-plots constructed using information theoretical methods from raw sensor data as a way for a robotic agent to characterise its interactions and interaction history. Measurements of the position and shape of the trajectories, including fractal dimension, can be used to characterise the agent-environment interaction.

We propose a motion generation model in which robots presume the sound source of an environmental sound and imitate its motion. Sharing environmental sounds between humans and robots enables them to share environmental information. It is difficult to transmit environmental sounds in human-robot communications. We approached this problem by focusing on the iconic gestures. Concretely, robots presume the motion of the sound source object and map it to the robot motion. This method enabled robots to imitate the motion of the sound source using their bodies.

Evolutionary Robotics is a promising approach to automatically build efficient controllers using stochastic optimization techniques. However, works in this area are often confronted to complex environments where even simple tasks cannot be achieved. In the scope of this paper, we propose an approach based on explicit problem decomposition and dynamic environment shaping to ease the learning task.

We present here an example of incremental learning between two computational models dealing with different modalities: a model allowing to switch spatial visual attention and a model allowing to learn the ordinal sequence of phonetical numbers. Their merging via a common reward signal allows anyway to produce a cardinal counting behaviour that can be implemented on a robot.

A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement.

This paper elaborates on the workplan of an initiative in embodied cognition: RobotCub. Our goal here is to provide background and to motivate our long-term plan of empirical research including brain and robotic sciences following the principles of epigenetic robotics.

In this paper, we propose a model to develop robots’ covert perceptual capability using reinforcement learning. Covert perceptual behavior is treated as action selected by a motivational system. We apply this model to vision-based navigation. The goal is to enable a robot to learn road boundary type. Instead of dealing with problems in controlled environments with a low-dimensional state space, we test the model on images captured in non-stationary environments. Incremental Hierarchical Discriminant Regression is used to generate states on the fly. Its coarse-to-fine tree structure guarantees real-time retrieval in high-dimensional state space. K Nearest-Neighbor strategy is adopted to further reduce training time complexity.

We describe a set of experiments investigating the role of natural language symbols in scaffolding situated action. Agents are evolved to respond appropriately to commands in order to perform simple tasks. We explore three different conditions, which show a significant advantage to the re-use of a public symbol system, through self-cueing leading to qualitative changes in performance. This is modelled by looping spoken output via environment back to heard input. We argue this work can be linked to, and sheds new light on, the account of self-directed speech advanced by the developmental psychologist Vygotsky in his model of the development of higher cognitive function.

This paper introduces a control architecture for the learning of complex sequence of gestures applied to autonomous robots. The architecture is designed to exploit the robot internal sensory-motor dynamics generated by visual, proprioceptive, and predictive informations in order to provide intuitive behaviors in the purpose of natural interactions with humans.

Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of abnormal social development. Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism.

In this study we investigated the effects of different morphological configurations on a robot swinging task using van der Pol oscillators. The task was examined using two separate degrees of freedom (DoF), both in the presence and absence of neural entrainment. Neural entrainment stabilises the system, reduces time-to-steady state and relaxes the requirement for a strong coupling with the environment in order to achieve mechanical entrainment. It was found that staged release of the distal DoF does not have any benefits over using both DoF from the onset of the experimentation. On the contrary, it is less efficient, both with respect to the time needed to reach a stable oscillatory regime and the maximum amplitude it can achieve. The same neural architecture is successful in achieving neuromechanical entrainment for a robotic walking task.

Humans use a set of exploratory procedures to examine object properties through grasping and touch. Our goal is to exploit similar methods with a humanoid robot to enable developmental learning about manipulation. We use a compliant robot hand to find objects without prior knowledge of their presence or location, and then tap those objects with a finger. This behavior lets the robot generate and collect samples of the contact sound produced by impact with that object. We demonstrate the feasibility of recognizing objects by their sound, and relate this to human performance under situations analogous to that of the robot.

Teaching a humanoid robot to reach for a visual target is a complex problem in part because of the high dimensionality of the control space. In this paper, we demonstrate a biologically plausible simplification of the reaching process that replaces the degrees of freedom in the neck of the robot with sensory readings from a vestibular system. We show that this simplification introduces errors that are easily overcome by a standard learning algorithm. Furthermore, the errors that are necessarily introduced by this simplification result in reaching trajectories that are curved in the same way as human reaching trajectories.

We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) autonomous development of values and goals, (4) bootstrapping of initial skills, (5) stability of skills, and (6) reproducibility. In this paper we: (a) provide a conceptual synthesis of ongoing emergence based on previous theorizing, (b) review current research in epigenetic robotics in light of ongoing emergence, (c) provide prototypical examples of ongoing emergence from infant development, and (d) outline computational issues relevant to creating robots exhibiting ongoing emergence.

This paper presents results from experimental studies on early language acquisition in infants and attempts to interpret the experimental results within the framework of the Ecological Theory of Language Acquisition (ETLA) recently proposed by (Lacerda et al., 2004a). From this perspective, the infant’s first steps in the acquisition of the ambient language are seen as a consequence of the infant’s general capacity to represent sensory input and the infant’s interaction with other actors in its immediate ecological environment. On the basis of available experimental evidence, it will be argued that ETLA offers a productive alternative to traditional descriptive views of the language acquisition process by presenting an operative model of how early linguistic function may emerge through interaction.

This article reviews two reflexive motor patterns in humans: Primitive reflexes and motor primitives. Both terms coexist in the literature of motor development and motor control, yet they are not synonyms. While primitive reflexes are a part of the temporary motor repertoire in early ontogeny, motor primitives refer to sets of motor patterns that are considered basic units of voluntary motor control thought to be present throughout the life-span. The article provides an overview of the anatomy and neurophysiology of human reflexive motor patterns to elucidate that both concepts are rooted in architecture of the spinal cord. I will advocate that an understanding of the human motor system that encompasses both primitive reflexes and motor primitives as well as the interaction with supraspinal motor centers will lead to an appreciation of the richness of the human motor repertoire, which in turn seems imperative for designing epigenetic robots and highly adaptable human machine interfaces.

How does an individual use the knowledge acquired through self exploration as a manipulable model through which to understand others and benefit from their knowledge? How can developmental and social learning be combined for their mutual benefit? In this paper we review a hierarchical architecture (HAMMER) which allows a principled way for combining knowledge through exploration and knowledge from others, through the creation and use of multiple inverse and forward models. We describe how Bayesian Belief Networks can be used to learn the association between a robot’s motor commands and sensory consequences (forward models), and how the inverse association can be used for imitation. Inverse models created through self exploration, as well as those from observing others can coexist and compete in a principled unified framework, that utilises the simulation theory of mind approach to mentally rehearse and understand the actions of others.

We present a Perception-Action architecture and experiments to simulate imprinting—the establishment of strong attachment links with a “caregiver”—in a robot. Following recent theories, we do not consider imprinting as rigidly timed and irreversible, but as a more flexible phenomenon that allows for further adaptation as a result of reward-based learning through experience. Our architecture reconciles these two types of perceptual learning traditionally considered as different and even incompatible. After the initial imprinting, adaptation is achieved in the context of a history of “affective” interactions between the robot and a human, driven by “distress” and “comfort” responses in the robot.

A model of gaze control is describes that includes mechanisms for predictive control using a forward model and event driven expectations of target behavior. The model roughly undergoes stages similar to those of human infants if the influence of the predictive systems is gradually increased.