The aim of the present paper is to propose that the adoption of a framework of biological development is suitable for the construction of artificial systems. We will argue that a developmental approach does provide unique insights on how to build highly complex and adaptable artificial systems. To illustrate our point, we will use as an example the acquisition of goal-directed reaching. In the initial part of the paper we will outline a) how mechanisms of biological development can be adapted to the artificial world, and b) how this artificial development differs from traditional engineering approaches to robotics. An experiment performed on an artificial system initially controlled by motor reflexes is presented, showing the acquisition of visuo-motor maps for ballistic control of reaching without explicit knowledge of the system's kinematic parameters.

Cleeremans: The search for the computational correlates of consciousness

Abstract. We present a modal logic called LIA (Logic of Intention and Attempt) in which we can reason about intention dynamics and intentional action execution. By exploiting the expressive power of LIA, we provide a formal analysis of the relation between intention and action and highlight the pivotal role of attempt in action execution. Besides, we deal with the problems of instrumental reasoning and intention persistence.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein’s (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm. Almost all developmental theories assume that a child’s interaction with the environment plays an important role in development. Often this interaction leads to long-term changes in behavior that can best be described as learning. Modern theories of learning characterize the process as exploratory, as involving the variation and selection of behavior or strategies, or as the discovery of

Abstract. Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor readings from an environment can be processed, a controller can be stabilized and thus the performance of a moving robot in a real-world environment is improved. So far, only experiments with artificially generated data have shown good results. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. This pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. Our results support the idea that learning with a liquid state machine can be applied not only to designed data but also to real, noisy data. 1

The theory of event coding (TEC) is a general framework explaining how perceived and produced events (stimuli and responses) are cognitively represented and how their representations interact to generate perception and action. This article discusses the implications of TEC for understanding the control of voluntary action and makes an attempt to apply, specify, and concretize the basic theoretical ideas in the light of the available research on action control. In particular, it is argued that the major control operations may take place long before a stimulus is encountered (the prepared-reflex principle), that stimulus-response translation may be more automatic than commonly thought, that action selection and execution are more interwoven than most approaches allow, and that the acquisition of action-contingent events (action effects) is likely to subserve both the selection and the evaluation of actions. Life inside and outside of psychological laboratories differs in many ways, which is particularly true with respect to action control. Outside the lab people seem to carry out actions to achieve particular goals and to adapt the environment according to their needs. Once they enter a lab, however, they are commonly talked into responding to arbitrary stimuli by carrying out meaningless movements. The latter is assumed to increase the amount of experimental control over the variables involved in performing an action, which of course is true and utterly important for disentangling all the confounds present in everyday actions. And yet, most models of action control seem to take this highly artificial

This book is about the interface between natural language and the sensorimotor system. It is obvious that there is an interface between language and sensorimotor cognition, because we can talk about what we see and do. The main proposal in the book is that the interface is more direct than is commonly assumed. To argue for this proposal I focus on a simple concrete episode—a man grabbing a cup—which can be reported in a simple transitive sentence (e.g. the English sentence The man grabbed a cup). In the first part of the book I present a detailed model of the sensorimotor processes involved in experiencing this episode, both as the agent bringing it about and as an observer watching it happen. The model draws on a large body of research in neuroscience and psychology. I also present a model of the syntactic structure of the associated transitive sentence, developed within the entirely separate discipline of theoretical linguistics. This latter model is a version of Chomsky’s ‘Minimalist ’ syntactic theory, which assumes that a sentence reporting the episode has the same underlying syntactic structure (called ‘logical form’) regardless of which language it is in. My main proposal is that these two independently motivated models are in fact closely

Abstract. The COTESYS cluster of excellence a investigates cognition for technical systems such as vehicles, robots, and factories. Cognitive technical systems (CTS) are information processing systems equipped with artificial sensors and actuators, integrated and embedded into physical systems, and acting in a physical world. They differ from other technical systems as they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accord with longterm intentions. Cognitive capabilities such as perception, reasoning, learning, and planning turn technical systems into systems that “know what they are doing”. The cognitive capabilities will result in systems of higher reliability, flexibility, adaptivity, and better performance. They will be easier to interact and cooperate with.

(a) A forward roll transformed to a dive roll. (b) A cartwheel retargeted to an Asimo-like robot. (c) A walk transformed onto a balance beam. Figure 1: Physically based motion transformation and retargeting. Human motions are the product of internal and external forces, but these forces are very difficult to measure in a general setting. Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces. The method employs a strategy based on randomized sampling of the control within user-specified bounds, coupled with forward dynamics simulation. Sampling-based techniques are well suited to this task because of their lack of dependence on derivatives, which are difficult to estimate in contact-rich scenarios. They are also easy to parallelize, which we exploit in our implementation on a compute cluster. We demonstrate reconstruction of a diverse set of captured motions, including walking, running, and contact rich tasks such as rolls and kip-up jumps. We further show how the method can be applied to physically based motion transformation and retargeting, physically plausible motion variations, and referencetrajectory-free idling motions. Alongside the successes, we point out a number of limitations and directions for future work. 1

sense of agency; sense of control Abstract:. The now growing literature on the content and sources of the phenomenology of first-person agency highlights the multi-faceted character of the phenomenology of agency and makes it clear that the experience of agency includes many other experiences as components. This paper examines the possible relations between these components of our experience of acting and the processes involved in action specification and action control. After a brief discussion of our awareness of our goals and means of action, it will focus on the sense of agency for a given action, understood as the sense the agent has that he or she is the author of that action. I argue that the sense of agency can be analyzed as a compound of more basic experiences, including the experience of intentional causation, the sense of initiation and the sense of control. I further argue that the sense of control may itself be analysed into a number of more specific, partially dissociable experiences. 1.