624 research outputs found
A digest of the main insights and achievements of the project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots
This document is directed to illustrate the main achievements and insights gained by the IM-CLeVeR project. These achievements suggest new hypothesis of research on intrinsic motivations and autonomous cumulative open-ended learning both for running new neuroscience and psychology experiments and for building future autonomously developing robots. IM-CLeVeR is a project funded by the European Commission under the 7th Framework Programme (FP7/2007-2013), \u27\u27Challenge 2 - Cognitive Systems, Interaction, Robotics\u27\u27, grant agreement No. ICTIP-231722
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Bio-inspired approaches to the control and modelling of an anthropomimetic robot
Introducing robots into human environments requires them to handle settings designed specifically for human size and morphology, however, large, conventional humanoid robots with stiff, high powered joint actuators pose a significant danger to humans. By contrast, “anthropomimetic” robots mimic both human morphology and internal structure; skeleton, muscles, compliance and high redundancy. Although far safer, their resultant compliant structure presents a formidable challenge to conventional control. Here we review, and seek to address, characteristic control issues of this class of robot, whilst exploiting their biomimetic nature by drawing upon biological motor control research. We derive a novel learning controller for discovering effective reaching actions created through sustained activation of one or more muscle synergies, an approach which draws upon strong, recent evidence from animal and humans studies, but is almost unexplored to date in musculoskeletal robot literature. Since the best synergies for a given robot will be unknown, we derive a deliberately simple reinforcement learning approach intended to allow their emergence, in particular those patterns which aid linearization of control. We also draw upon optimal control theories to encourage the emergence of smoother movement by incorporating signal dependent noise and trial repetition.
In addition, we argue the utility of developing a detailed dynamic model of a complete robot and present a stable, physics-‐‑based model, of the anthropomimetic ECCERobot,
running in real time with 55 muscles and 88 degrees of freedom.
Using the model, we find that effective reaching actions can be learned which employ only two sequential motor co-‐‑activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-‐‑activations can be reconstructed to significant accuracy using weighted combinations of only 13 common fragments, labelled “candidate synergies”. Using these synergies as drivable units the same controller learns the same task both faster and better, however, other reaching tasks perform less well, proportional to dissimilarity; we therefore propose that modifications enabling emergence of a more generic set of synergies are required.
Finally, we propose a continuous controller for the robot, based on model predictive control, incorporating our model as a predictive component for state estimation, delay-‐‑
compensation and planning, including merging of the robot and sensed environment into a single model. We test the delay compensation mechanism by controlling a second copy of the model acting as a proxy for the real robot, finding that performance is significantly improved if a precise degree of compensation is applied and show how rapidly an un-‐‑compensated controller fails as the model accuracy degrades
Challenges for artificial cognitive systems
The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out our neck and formulate the challenges for artificial cognitive systems. These challenges are articulated in terms of a definition of what a cognitive system is: a system that learns from experience and uses its acquired knowledge (both declarative and practical) in a flexible manner to achieve its own goals
TOWARDS THE GROUNDING OF ABSTRACT CATEGORIES IN COGNITIVE ROBOTS
The grounding of language in humanoid robots is a fundamental problem, especially
in social scenarios which involve the interaction of robots with human beings. Indeed,
natural language represents the most natural interface for humans to interact
and exchange information about concrete entities like KNIFE, HAMMER and abstract
concepts such as MAKE, USE. This research domain is very important not
only for the advances that it can produce in the design of human-robot communication
systems, but also for the implication that it can have on cognitive science.
Abstract words are used in daily conversations among people to describe events and
situations that occur in the environment. Many scholars have suggested that the
distinction between concrete and abstract words is a continuum according to which
all entities can be varied in their level of abstractness.
The work presented herein aimed to ground abstract concepts, similarly to concrete
ones, in perception and action systems. This permitted to investigate how different
behavioural and cognitive capabilities can be integrated in a humanoid robot in
order to bootstrap the development of higher-order skills such as the acquisition of
abstract words. To this end, three neuro-robotics models were implemented.
The first neuro-robotics experiment consisted in training a humanoid robot to perform
a set of motor primitives (e.g. PUSH, PULL, etc.) that hierarchically combined
led to the acquisition of higher-order words (e.g. ACCEPT, REJECT). The
implementation of this model, based on a feed-forward artificial neural networks,
permitted the assessment of the training methodology adopted for the grounding of
language in humanoid robots.
In the second experiment, the architecture used for carrying out the first study
was reimplemented employing recurrent artificial neural networks that enabled the
temporal specification of the action primitives to be executed by the robot. This
permitted to increase the combinations of actions that can be taught to the robot
for the generation of more complex movements.
For the third experiment, a model based on recurrent neural networks that integrated
multi-modal inputs (i.e. language, vision and proprioception) was implemented for
the grounding of abstract action words (e.g. USE, MAKE). Abstract representations
of actions ("one-hot" encoding) used in the other two experiments, were replaced
with the joints values recorded from the iCub robot sensors.
Experimental results showed that motor primitives have different activation patterns
according to the action's sequence in which they are embedded. Furthermore, the
performed simulations suggested that the acquisition of concepts related to abstract
action words requires the reactivation of similar internal representations activated
during the acquisition of the basic concepts, directly grounded in perceptual and
sensorimotor knowledge, contained in the hierarchical structure of the words used
to ground the abstract action words.This study was financed by the EU project RobotDoC (235065) from the Seventh
Framework Programme (FP7), Marie Curie Actions Initial Training Network
Design of a Bio-Inspired 3D Orientation Coordinate System and Application in Robotised Tele-Sonography
International audienc
- …