To be published in Behavioral and Brain Sciences (in press)

Praxic functions are frequently altered following brain lesion, giving rise to apraxia, a complex pattern of impairments that is difficult to assess or interpret. In this chapter, we review the current taxonomies of apraxia and related cognitive and neuropsychological models. We also address the questions of the neuroanatomical correlates of apraxia, the relation between apraxia and aphasia and the analysis of apraxic errors. We provide a possible explanation for the difficulties encountered in investigating apraxia and also several approaches to overcome them, such as systematic investigation and modeling studies. Finally, we argue for a multidisciplinary approach. For example, apraxia should be studied in consideration with and could contribute to other fields such as normal motor control, neuroimaging and neurophysiology.

One of the fundamental problems in imitation is the correspondence problem, how to map between the actions, states and effects of the model and imitator agents, when the embodiment of the agents is dissimilar. In our approach, the matching is according to different metrics and granularity. This paper presents JABBERWOCKY, a system that uses captured data from a human demonstrator to generate appropriate action commands, addressing the correspondence problem in imitation. Towards a characterization of the space of effect metrics, we are exploring absolute/relative angle and displacement aspects and focus on the overall arrangement and trajectory of manipulated objects. Using as an example a captured demonstration from a human, the system produces a correspondence solution given a selection of effect metrics and starting from dissimilar initial object positions, producing action commands that are then executed by two imitator target platforms (in simulation) to successfully imitate. 1

One of the fundamental problems in imitation is the correspondence problem, how to map between the actions, states and effects of the model and imitator agents, when the embodiment of the agents is dissimilar. In our approach, the matching is according to different metrics and granularity. This paper presents JABBERWOCKY, a system that uses captured data from a human demonstrator to generate appropriate action commands, addressing the correspondence problem in imitation. Towards a characterization of the space of effect metrics, we are exploring absolute/relative angle and displacement aspects and focus on the overall arrangement and trajectory of manipulated objects. Using as an example a captured demonstration from a human, the system produces a correspondence solution given a selection of effect metrics and starting from dissimilar initial object positions, producing action commands that are then executed by two imitator target platforms (in simulation) to successfully imitate.

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.

Imitation o#ers a powerful mechanism for knowledge acquisition, particularly for intelligent agents (like infants) that lack the ability to transfer knowledge using language. Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals o#er a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. In this paper, we present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. We develop Bayesian algorithms, based on Meltzo# and Moore's AIM hypothesis for infant imitation, that implement the core of an imitation learning framework. Our algorithms are computationally e#cient, allowing real-time learning and imitation in an active stereo vision robotic head. We present results of both software simulations and our algorithms running on the head, demonstrating the validity of our approach. We conclude by advocating a research agenda that Preprint submitted to Elsevier Science 4 October 2004 promotes interaction between cognitive and robotic studies of imitation.

There are two functional elements used by humans to understand and perform actions. These elements are: the body schema and the body percept. The first one is a representation of the body that contains information of the body's capabilities. The body percept is a snapshot of the body and its relation with the environment at a given instant. These elements are believed to interact between them generating among other abilities, i.e. the ability to imitate. This paper presents an approach to robot imitation based on these two functional elements. Our approach is gradually expanded by using four developmental stages of imitation on humans as a guideline.

Humans and animals use imitation as a mechanism for acquiring knowledge. Recently, several algorithms and models have been proposed for imitation learning in robots and humans. However, few proposals o#er a framework for imitation learning in a stochastic environment where the imitator must learn and act under realtime performance constraints. We present a probabilistic framework for imitation learning in stochastic environments with unreliable sensors. We develop Bayesian algorithms, based on Meltzo# and Moore's AIM hypothesis for infant imitation, that implement the core of an imitation learning framework, and sketch basic proposals for the other components. Our algorithms are computationally e#cient, allowing real-time learning and imitation in an active stereo vision robotic head. We present results of both software simulations and our algorithms running on the head, demonstrating the validity of our approach.

There are two functional elements used by humans to understand and perform actions. These elements are: the body schema and the body percept. The first one is a representation of the body that contains information of the body's capabilities. The second one is a snapshot of the body and its relation with the environment at a given instant. These elements interact in order to generate, among other abilities, the ability to imitate. This paper presents an approach to robot imitation based on these two functional elements. Our approach is gradually expanded throughout three developmental stages used by humans to refine imitation. Experimental results are presented to support the feasibility of the proposed approach at the current stage for 2D movements and simple manipulation actions.

Abstract not found