216 research outputs found
Asimov's Coming Back
Ever since the word âROBOTâ first appeared in a science\ud
fiction in 1921, scientists and engineers have been trying\ud
different ways to create it. Present technologies in\ud
mechanical and electrical engineering makes it possible\ud
to have robots in such places as industrial manufacturing\ud
and assembling lines. Although they are\ud
essentially robotic arms or similarly driven by electrical\ud
power and signal control, they could be treated the\ud
primitive pioneers in application. Researches in the\ud
laboratories go much further. Interdisciplines are\ud
directing the evolution of more advanced robots. Among these are artificial\ud
intelligence, computational neuroscience, mathematics and robotics. These disciplines\ud
come closer as more complex problems emerge.\ud
From a robotâs point of view, three basic abilities are needed. They are thinking\ud
and memory, sensory perceptions, control and behaving. These are capabilities we\ud
human beings have to adapt ourselves to the environment. Although\ud
researches on robots, especially on intelligent thinking, progress slowly, a revolution\ud
for biological inspired robotics is spreading out in the laboratories all over the world
Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot
Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments
Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons
The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. © 2014 Toutounji and Pasemann
VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition
Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing
thanks to their potential energy-efficiency, low latencies, and capacity for
continual learning. While these capabilities are well suited for robotics
tasks, SNNs have seen limited adaptation in this field thus far. This work
introduces a SNN for Visual Place Recognition (VPR) that is both trainable
within minutes and queryable in milliseconds, making it well suited for
deployment on compute-constrained robotic systems. Our proposed system,
VPRTempo, overcomes slow training and inference times using an abstracted SNN
that trades biological realism for efficiency. VPRTempo employs a temporal code
that determines the timing of a single spike based on a pixel's intensity, as
opposed to prior SNNs relying on rate coding that determined the number of
spikes; improving spike efficiency by over 100%. VPRTempo is trained using
Spike-Timing Dependent Plasticity and a supervised delta learning rule
enforcing that each output spiking neuron responds to just a single place. We
evaluate our system on the Nordland and Oxford RobotCar benchmark localization
datasets, which include up to 27k places. We found that VPRTempo's accuracy is
comparable to prior SNNs and the popular NetVLAD place recognition algorithm,
while being several orders of magnitude faster and suitable for real-time
deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be
integrated as a loop closure component for online SLAM on resource-constrained
systems such as space and underwater robots.Comment: 8 pages, 3 figures, accepted to the IEEE International Conference on
Robotics and Automation (ICRA) 202
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost
The brain's connective core and its role in animal cognition
This paper addresses the question of how the brain of an animal achieves cognitive integrationâthat is to say how it manages to bring its fullest resources to bear on an ongoing situation. To fully exploit its cognitive resources, whether inherited or acquired through experience, it must be possible for unanticipated coalitions of brain processes to form. This facilitates the novel recombination of the elements of an existing behavioural repertoire, and thereby enables innovation. But in a system comprising massively many anatomically distributed assemblies of neurons, it is far from clear how such open-ended coalition formation is possible. The present paper draws on contemporary findings in brain connectivity and neurodynamics, as well as the literature of artificial intelligence, to outline a possible answer in terms of the brain's most richly connected and topologically central structures, its so-called connective core
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
- âŠ