Abstract. This paper proposes cognitive developmental robotics as a new principle for the design of humanoid robots. This principle may provide ways of understanding human beings that go beyond the current level of explanation found in the natural and social sciences. Furthermore, a methodological emphasis on humanoid robots in the design of artificial creatures holds promise because they have many degrees of freedom and sense modalities and, thus, must face the challenges of scalability that are often side stepped in simpler domains. We examine the potential of this new principle as well as issues that are likely to be important to CDR in the future. 1

We propose an approach for a robot to imitate th gestures of ah uman demonstrator. Our framework consists solely of two components: a Sensory-Motor Map (SMM) and a View-Point Transformation (VP ). h SMM establish an association between an arm image and th corresponding joint angles and it is learned byth system during a period of observation of its own gestures.h VP is widely discussed in th psychycT of visual perception and is used to transform th image of th demonstrator's arm to th so-called ego-centric image, as if th robot were observing its own arm. Di#erent structures of th SMM and VP are proposed in accordancewith observations in h uman imitation.h whti system relies on monocular visual information and leads to a parsimonious archTT for learning by imitation. Real-time results are presented and discussed.

Abstract not found

In this paper, we combine the formal methods from reinforcement learning with the paradigm of imitation learning. The extension of the reinforcement learning framework to integrate the information provided by an expert (demonstrator) has the important advantage of allowing a clear decrease of the time necessary to learn certain robotic tasks. Hence, learning by imitation can be interpreted as a mechanism for fast skill transfer. Another contribution of thispaper consists in showing that our formalism is able to model different types of imitation-learning that are described in the biological literature. It thus unifies in the same abstract model what used to be addressed as separate behavioral patterns. We illustrate the application of these methods in simulation and with a real robot.

This chapter reviews several approaches to the problem of learning by imitation in robotics. We start by describing several cognitive processes identified in the literature as necessary for imitation. We then proceed by surveying different approaches to this problem, placing particular emphasys on methods whereby an agent first learns about its own body dynamics by means of self-exploration and then uses this knowledge about its own body to recognize the actions being performed by other agents. This general approach is related to the motor theory of perception, particularly to the mirror neurons found in primates. We distinguish three fundamental classes of methods, corresponding to three abstraction levels at which imitation can be addressed. As such, the methods surveyed herein exhibit behaviors that range from raw sensory-motor trajectory matching to high-level abstract task replication. We also discuss the impact that knowledge about the world and/or the demonstrator can have on the particular behaviors exhibited.

This dissertation presents an approach to robot programming by demonstration based on two key concepts: demonstrator intent is the most meaningful signal that the robot can observe, and the robot should have a basic level of behavioral competency from which to interpret observed actions. Intent is a teleological, robust teaching signal invariant to many common sources of noise in training. The robot can use the knowledge encapsulated in sensorimotor schemas to interpret the demonstration. Furthermore, knowledge gained in prior demonstrations can be applied to future sessions. iv I argue that programming by demonstration be organized into declarative and procedural components. The declarative component represents a reusable outline of underlying behavior that can be applied to many different contexts. The procedural component represents the dynamic portion of the task that is based on features observed at run time. I describe how statistical models, and Bayesian methods in particular, can be used to model these components. These models have many features that are beneficial for learning in this domain, such as tolerance for uncertainty, and the ability to incorporate prior knowledge into inferences. I demonstrate this architecture through experiments on a bimanual humanoid robot using tasks from the pick and place domain.