495 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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
Towards the Grounding of Abstract Words: A Neural Network Model for Cognitive Robots
In this paper, a model based on Artificial Neural Networks (ANNs) extends the symbol grounding mechanism toabstract words for cognitive robots. The aim of this work is to obtain a semantic representation of abstract concepts through the grounding in sensorimotor experiences for a humanoid robotic platform. Simulation experiments have been developed on a software environment for the iCub robot. Words that express general actions with a sensorimotor component are first taught to the simulated robot. During the training stage the robot first learns to perform a set of basic action primitives through the mechanism of direct grounding. Subsequently, the grounding of action primitives, acquired via direct sensorimotor experience, is transferred to higher-order words via linguistic descriptions. The idea is that by combining words grounded in sensorimotor experience the simulated robot can acquire more abstract concepts. The experiments aim to teach the robot the meaning of abstract words by making it experience sensorimotor actions. The iCub humanoid robot will be used for testing experiments on a real robotic architecture
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction
Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments.
This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks.
Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration
Ongoing Emergence: A Core Concept in Epigenetic Robotics
We propose ongoing emergence as a core concept in
epigenetic robotics. Ongoing emergence refers to the
continuous development and integration of new skills
and is exhibited when six criteria are satisfied: (1)
continuous skill acquisition, (2) incorporation of new
skills with existing skills, (3) autonomous development
of values and goals, (4) bootstrapping of initial skills, (5)
stability of skills, and (6) reproducibility. In this paper
we: (a) provide a conceptual synthesis of ongoing
emergence based on previous theorizing, (b) review
current research in epigenetic robotics in light of ongoing
emergence, (c) provide prototypical examples of ongoing
emergence from infant development, and (d) outline
computational issues relevant to creating robots
exhibiting ongoing emergence
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