This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot’s hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot.

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: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) 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.

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

Abstract- We present a connectionist model that combines motions and language based on the behavioral experiences of a real robot. Two models of recurrent neural network with parametric bias (RNNPB) were trained using motion sequences and linguistic sequences. These sequences were combined using their respective parameters so that the robot could handle many-to-many relationships between motion sequences and linguistic sequences. Motion sequences were articulated into some primitives corresponding to given linguistic sequences using the prediction error of the RNNPB model. The experimental task in which a humanoid robot moved its arm on a table demonstrated that the robot could generate a motion sequence corresponding to given linguistic sequence even if the motions or sequences were not included in the training data, and vice versa. I.

The current paper shows a neuro-robotics experiment on developmental learning of goal-directed actions. The robot was trained to predict visuo-proprioceptive flow of achieving a set of goal-directed behaviors through iterative tutor training processes. The learning was conducted by employing a dynamic neural network model which is characterized by their multiple time-scale dynamics. The experimental results showed that functional hierarchical structures emerge through stages of developments where behavior primitives are generated in earlier stages and their sequences of achieving goals appear in later stages. It was also observed that motor imagery is generated in earlier stages compared to actual behaviors. Our claim that manipulatable inner representation should emerge through the sensory–motor interactions is corresponded to Piaget’s constructivist view.

Recent advances in different disciplines, ranging from cognitive sciences and robotics, biology and neurosciences, to social sciences and philosophy are clarifying that intelligence resides in the circular relationship between the brain of an individual organism, its body, and the environment (including the social environment).

In order for robots to engage in dialog with human teammates, they must have the ability to identify correspondences between elements of language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet. ” This article describes several of our results that use probabilistic inference to address the symbol grounding problem. Our approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, then discuss our new framework, Generalized Grounding Graphs (G 3). The G 3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments in which the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation. 1

Abstract. In this chapter we introduce the area of research that attempts to study the evolution of communication in embodied agents through adaptive techniques, such us artificial evolution. More specifically, we illustrate the theoretical assumptions behind this type of research, we present the methods that can be used to realize embodied and communicating artificial agents, and we discuss the main research challenges and the criteria for evaluating progresses in this field. 1

Reading as active sensing: a computational model of gaze

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