814 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
Reservoir Computing Architectures for Modeling Robot Navigation Systems
This thesis proposes a new efficient and biologically inspired way of modeling navigation tasks for autonomous mobile robots having restrictions on cost, energy consumption, and computational complexity (such as household and assistant robots). It is based on the recently proposed Reservoir Computing approach for training Recurrent Neural Networks.
Robot Navigation Systems
Autonomous mobile robots must be able to safely and purposefully navigate in complex dynamic environments, preferentially considering a restricted amount of computational power as well as limited energy consumption. In order to turn these robots into commercially viable domestic products with intelligent, abstract computational capabilities, it is also necessary to use inexpensive sensory apparatus such as a few infra-red distance sensors of limited accuracy.
Current state-of-the-art methods for robot localization and navigation require fully equipped robotic platforms usually possessing expensive laser scanners for environment mapping, a considerable amount of computational power, and extensive explicit modeling of the environment and of the task.
This thesis
The research presented in this thesis is a step towards creating intelligent autonomous mobile robots with abstract reasoning capabilities using a limited number of very simple raw noisy sensory signals, such as distance sensors. The basic assumption is that the low-dimensional sensory signal can be projected into a high-dimensional dynamic space where learning and computation is performed by linear methods (such as linear regression), overcoming sensor aliasing problems commonly found in robot navigation tasks. This form of computation is known in the literature as Reservoir Computing (RC), and the Echo State Network is a particular RC model used in this work and characterized by having the high-dimensional space implemented by a discrete analog recurrent neural network with fading memory properties. This thesis proposes a number of Reservoir Computing architectures which can be used in a variety of autonomous navigation tasks, by modeling implicit abstract representations of an environment as well as navigation behaviors which can be sequentially executed in the physical environment or simulated as a plan in deliberative goal-directed tasks.
Navigation attractors
A navigation attractor is a reactive robot behavior defined by a temporal pattern of sensory-motor coupling through the environment space. Under this scheme, a robot tends to follow a trajectory with attractor-like characteristics in space. These navigation attractors are characterized by being robust to noise and unpredictable events and by having inherent collision avoidance skills.
In this work, it is shown that an RC network can model not only one behavior, but multiple navigation behaviors by shifting the operating point of the dynamical reservoir system into different \emph{sub-space attractors} using additional external inputs representing the selected behavior. The sub-space attractors emerge from the coupling existing between the RC network, which controls the autonomous robot, and the environment. All this is achieved under an imitation learning framework which trains the RC network using examples of navigation behaviors generated by a supervisor controller or a human.
Implicit spatial representations
From the stream of sensory input given by distance sensors, it is possible to construct implicit spatial representations of an environment by using Reservoir Computing networks. These networks are trained in a supervised way to predict locations at different levels of abstraction, from continuous-valued robot's pose in the global coordinate's frame, to more abstract locations such as small delimited areas and rooms of a robot environment. The high-dimensional reservoir projects the sensory input into a dynamic system space, whose characteristic fading memory disambiguates the sensory space, solving the sensor aliasing problems where multiple different locations generate similar sensory readings from the robot's perspective.
Hierarchical networks for goal-directed navigation
It is possible to model navigation attractors and implicit spatial representations with the same type of RC network.
By constructing an hierarchical RC architecture which combines the aforementioned modeling skills in two different reservoir modules operating at different timescales, it is possible to achieve complex context-dependent sensory-motor coupling in unknown environments. The general idea is that the network trained to predict the location and orientation of the robot in this architecture can be used to select appropriate navigation attractors according to the current context, by shifting the operating point of the navigation reservoir to a sub-space attractor.
As the robot navigates from one room to the next, a corresponding context switch selects a new reactive navigation behavior. This continuous sequence of context switches and reactive behaviors, when combined with an external input indicating the destination room, leads ultimately to a goal-directed navigation system, purely trained in a supervised way with examples of sensory-motor coupling.
Generative modeling of environment-robot dynamics
RC networks trained to predict the position of the robot from the sensory signals learns forward models of the robot.
By using a generative RC network which predicts not only locations but also sensory nodes, it is possible to use the network in the opposite direction for predicting local environmental sensory perceptions from the robot position as input, thus learning an inverse model.
The implicit map learned by forward models can be made explicit, by running the RC network in reverse: predict the local sensory signals given the location of the robot as input (inverse model).
which are fed back to the reservoir, it is possible to
internally predict future scenarios and behaviors without actually experiencing them in the current environment (a process analogous to dreaming), constituting a planning-like capability which opens new possibilities for deliberative navigation systems.
Unsupervised learning of spatial representations
In order to achieve a higher degree of autonomy in the learning process of RC-based navigation systems which use implicit learned models of the environment for goal-directed navigation, a new architecture is proposed. Instead of using linear regression, an unsupervised learning method which extracts slowly-varying output signals from the reservoir states, called Slow Feature Analysis, is used to generate self-organized spatial representations at the output layer, without the requirement of labeling training data with the desired locations.
It is shown experimentally that the proposed RC-SFA architecture is empowered with an unique combination of short-term memory and non-linear transformations which overcomes the hidden state problem present in robot navigation tasks. In addition, experiments with simulated and real robots indicate that spatial activations generated by the trained network show similarities to the activations of CA1 hippocampal cells of rats (a specific group of neurons in the hippocampus)
System modeling with Reservoir Computing
Reservoir Computing is a novel technique which can be applied to a wide range of applications. In this work we demonstrate that Reservoir Computing can be used for black box nonlinear system modeling.We will use Reservoir Computing to model the output flow of a heating tank with variable deadtime
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Digital Twin Modeling And Optimal Control Of Soft-Bodied Robotics Using Reservoir Computing
Soft-bodied robots have become increasingly popular due to their ability to per- form tasks that are difficult or impossible for traditional rigid robots. However, accurately modeling and controlling the movement and behavior of soft robots are very challenging due to their complex and dynamic nature. In recent years, Reservoir Computing has emerged as a promising approach to modeling and controlling soft robots. In this thesis, reservoir computing was used to create a digital twin of soft-bodied robots. Specifically, a digital twin of a spring-mass system was created using echo state network, a popular reservoir computing model. Furthermore, an optimal controller was trained using reservoir computing to drive the spring-mass system to follow a desired trajectory. Extensive simulations were carried out to validate the proposed methods. The results demonstrate the effectiveness of the proposed approach. For example, the digital twin model achieved 2% MAPE and the optimal controller achieved 8.7% MAPE for a 20-node 54-spring system.
Index Terms: Deep Learning, Digital Twin Modeling, Echo State Network, Optimal control, Soft Bodied Robotics, Time series predictio
06031 Abstracts Collection -- Organic Computing -- Controlled Emergence
Organic Computing has emerged recently as a challenging vision for
future information processing systems, based on the insight that we
will soon be surrounded by large collections of autonomous systems
equipped with sensors and actuators to be aware of their environment,
to communicate freely, and to organize themselves in order to perform
the actions and services required. Organic Computing Systems will
adapt dynamically to the current conditions of its environment, they
will be self-organizing, self-configuring, self-healing,
self-protecting, self-explaining, and context-aware.
From 15.01.06 to 20.01.06, the Dagstuhl Seminar 06031 ``Organic
Computing -- Controlled Emergence\u27\u27 was held in the International
Conference and Research Center (IBFI), Schloss Dagstuhl.
The seminar was characterized by the very constructive search for
common ground between engineering and natural sciences, between
informatics on the one hand and biology, neuroscience, and chemistry
on the other. The common denominator was the objective to build
practically usable self-organizing and emergent systems or their
components.
An indicator for the practical orientation of the seminar was the
large number of OC application systems, envisioned or already under
implementation, such as the Internet, robotics, wireless sensor
networks, traffic control, computer vision, organic systems on chip,
an adaptive and self-organizing room with intelligent sensors or
reconfigurable guiding systems for smart office buildings. The
application orientation was also apparent by the large number of
methods and tools presented during the seminar, which might be used as
building blocks for OC systems, such as an evolutionary design
methodology, OC architectures, especially several implementations of
observer/controller structures, measures and measurement tools for
emergence and complexity, assertion-based methods to control
self-organization, wrappings, a software methodology to build
reflective systems, and components for OC middleware.
Organic Computing is clearly oriented towards applications but is
augmented at the same time by more theoretical bio-inspired and
nature-inspired work, such as chemical computing, theory of complex
systems and non-linear dynamics, control mechanisms in insect swarms,
homeostatic mechanisms in the brain, a quantitative approach to
robustness, abstraction and instantiation as a central metaphor for
understanding complex systems.
Compared to its beginnings, Organic Computing is coming of age. The OC
vision is increasingly padded with meaningful applications and usable
tools, but the path towards full OC systems is still complex. There is
progress in a more scientific understanding of emergent processes. In
the future, we must understand more clearly how to open the
configuration space of technical systems for on-line
modification. Finally, we must make sure that the human user remains
in full control while allowing the systems to optimize
Simple and complex behavior learning using behavior hidden Markov model and CobART
This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categorized using correlation based adaptive resonance theory (CobART) network that generates motion primitives corresponding to robot's base abilities. In the behavior modeling phase, a modified version of hidden Markov model (HMM), is called Behavior-HMM, is used to model the relationships among the motion primitives in a finite state stochastic network. At the same time, a motion generator which is an artificial neural network (ANN) is trained for each motion primitive to learn essential robot motor commands. In the behavior generation phase, a motion primitive sequence that can perform the desired task is generated according to the previously learned Behavior-HMMs at the higher level. Then, in the lower level, these motion primitives are executed by the motion generator which is specifically trained for the corresponding motion primitive. The transitions between the motion primitives are done according to observed sensory data and probabilistic weights assigned to each transition during the learning phase. The proposed models are not constructed for one specific behavior, but are intended to be bases for all behaviors. The behavior learning capabilities of the model is extended by integrating previously learned behaviors hierarchically which is referred as CBLM. Hence, new behaviors can take advantage of already discovered behaviors. Performed experiments on a robot simulator show that simple and complex-behavior learning models can generate requested behaviors effectively
An experimental characterization of reservoir computing in ambient assisted living applications
In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks
Material properties affect evolution's ability to exploit morphological computation in growing soft-bodied creatures
The concept of morphological computation holds that the
body of an agent can, under certain circumstances, exploit
the interaction with the environment to achieve useful behavior,
potentially reducing the computational burden of
the brain/controller. The conditions under which such phenomenon
arises are, however, unclear. We hypothesize that
morphological computation will be facilitated by body plans
with appropriate geometric, material, and growth properties,
while it will be hindered by other body plans in which one or
more of these three properties is not well suited to the task.
We test this by evolving the geometries and growth processes
of soft robots, with either manually-set softer or stiffer material
properties. Results support our hypothesis: we find that
for the task investigated, evolved softer robots achieve better
performances with simpler growth processes than evolved
stiffer ones. We hold that the softer robots succeed because
they are better able to exploit morphological computation.
This four-way interaction among geometry, growth, material
properties and morphological computation is but one example
phenomenon that can be investigated using the system here
introduced, that could enable future studies on the evolution
and development of generic soft-bodied creatures
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