Proximodistal structure of early reaching in human infants

Berthier, Neil E.; Clifton, Rachel K.; McCall, Daniel D.; Robin, Daniel J.

doi:10.1007/s002210050795

Cited by 104 publications

(100 citation statements)

References 22 publications

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“…3. mode(3): in p 3 % (typically p 3 = 10%), it performs a random experiment inside the region where the mean competence level is the lowest. Developmental Constraints for the Reduction of the Initiation Set: to improve the quality of the learnt inverse model, we add a heuristic inspired by observations of Berthier et al (Berthier et al, 1999) who noticed that infant's reaching attempts were often preceded by movements that either elevated their hand or moved their hand back to their side. By analogy, using such heuristics can directly allow a highly-redundant robotic system to reduce the space of initiation states used to learn to reach goals, and also typically prevent it from experimenting with too complex actuator configurations.…”

Section: Mode(2)mentioning

confidence: 99%

Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints

Oudeyer¹,

Baranès²,

Kaplan

2012

Intrinsically Motivated Learning in Natural and Artificial Systems

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Abstract. Open-ended exploration and learning in the real world is a major challenge of developmental robotics. Three properties of real-world sensorimotor spaces provide important conceptual and technical challenges: unlearnability, high-dimensionality and unboundedness. In this chapter, we argue that exploration in such spaces needs to be constrained and guided by several combined developmental mechanisms. While intrinsic motivation, i.e. curiosity-driven learning, is a key mechanism to address this challenge, it has to be complemented and integrated with other developmental constraints, in particular: sensorimotor primitives and embodiment, task space representations, maturational processes (i.e. adaptive changes of the embodied sensorimotor apparatus), and social guidance. We illustrate and discuss the potential of such an integration of developmental mechanisms in several robot learning experiments.A central aim of developmental robotics is to study the developmental mechanisms that allow life-long and open-ended learning of new skills and new knowledge in robots and animals (Asada et al., 2009;Lungarella et al., 2003;Weng et al., 2001). Strongly rooted in theories of human and animal development, embodied computational models are built both to explore how one could build more versatile and adaptive robots, as in the work presented in this chapter, and to explore new understandings of biological development (Oudeyer, 2010).Building machines capable of open-ended learning in the real world poses many difficult challenges. One of them is exploration, which is the central topic of this chapter. In order to be able to learn cumulatively an open-ended repertoire of skills, developmental robots, like animal babies and human infants, shall be equipped with task-independent mechanisms which push them to explore new activities and new situations. However, a major problem is that the continuous sensorimotor space of a typical robot, including its own body as well as all the potential interactions with the open-ended surrounding physical and social environment, is extremely large and high-dimensional. The set of skills that can potentially be learnt is actually infinite. Yet, within a life-time, only a small subset of them can be practiced and learnt. Thus the central question: how to explore and what to learn? And with this question comes an equally important question: What not to explore and what not to learn? Clearly, exploring randomly and/or trying to learn all possible sensorimotor skills will fail.

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Section: Mode(2)mentioning

confidence: 99%

Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints

Oudeyer¹,

Baranès²,

Kaplan

2012

Intrinsically Motivated Learning in Natural and Artificial Systems

View full text Add to dashboard Cite

show abstract

“…A similar mechanism is also used whenever the robot is not able to detect its end-effector, but in this case a coupled "head-arm reflex" is implemented, which drives both into an initial position. A similar mechanism is implemented in [18], inspired by [19]. All these elements cause the duration of each asterisk to be variable in practice with respect to a nominal performance, since in each trial minor changes in the learnt model can trigger one or more undesirable effects.…”

Section: B Performance Metricsmentioning

confidence: 99%

Autonomous online learning of velocity kinematics on the iCub: A comparative study

Droniou

Ivaldi

Padois

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In the last years, several regression algorithms have been proposed to learn accurate mechanical models of robots. Comparisons are proposed at the conceptual level or through the use of recorded databases, but they deliver limited conclusions with respect to the real performance of these algorithms in their true context of use, i.e. online learning on the real robot interacting with its environment, within a feedback control loop. In this paper, we provide an empirical study of three state-of-the-art regression methods through online learning on the iCub robot holding a tool. We show that they can effectively learn a visuo-motor kinematic model for a simple visual servoing task in a very limited time (few minutes), without making any a priori hypothesis on the geometry of the robot and its tool. Furthermore, we can draw from the results some stronger conclusions about the comparison of the algorithms than previous studies based on databases.

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“…These multijoint sites could play a role in encoding interjoint synergies. The proximal to distal progression in motor map development is reminiscent to the proximal to distal control strategy of human infants during arm movement development (Berthier et al 1999). …”

Section: Normal and Use Development Of The M1 Mapmentioning

confidence: 99%

Activity- and use-dependent plasticity of the developing corticospinal system☆

Martin

Friel

Salimi

et al. 2007

Neuroscience & Biobehavioral Reviews

132

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The corticospinal system (CS), critical for controlling skilled movements, develops during the late prenatal and early postnatal periods in all species examined. In the cat, there is a sequence of development of the mature pattern of terminations of corticospinal tract axons in the spinal gray matter, followed by motor map development of the primary motor cortex. Skilled limb movements begin to be expressed as the map develops. Development of the proper connections between CS axons and spinal neurons in cats depends on CS neural activity and motor behavioral experience during a critical postnatal period. Reversible CS inactivation or preventing limb use produces an aberrant distribution of CS axon terminations and impairs visually guided movements. This altered pattern of CS connections after inactivation in cats resembles the aberrant pattern of motor responses evoked by transcranial magnetic stimulation in hemiplegic cerebral palsy patients. Left untreated, these impairments do not resolve. We have found that activity-dependent processes can be harnessed in cats to reestablish normal CS connections and function. This finding suggests that CS connectivity and function might some day be restored in hemiplegic cerebral palsy.

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Proximodistal structure of early reaching in human infants

Cited by 104 publications

References 22 publications

Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints

Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints

Autonomous online learning of velocity kinematics on the iCub: A comparative study

Activity- and use-dependent plasticity of the developing corticospinal system☆

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