4,151 research outputs found
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Uncalibrated Dynamic Mechanical System Controller
An apparatus and method for enabling an uncalibrated, model independent controller for a mechanical system using a dynamic quasi-Newton algorithm which incorporates velocity components of any moving system parameter(s) is provided. In the preferred embodiment, tracking of a moving target by a robot having multiple degrees of freedom is achieved using an uncalibrated model independent visual servo control. Model independent visual servo control is defined as using visual feedback to control a robot's servomotors without a precisely calibrated kinematic robot model or camera model. A processor updates a Jacobian and a controller provides control signals such that the robot's end effector is directed to a desired location relative to a target on a workpiece.Georgia Tech Research Corporatio
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
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
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