1,198 research outputs found
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
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
Evolutionary robotics and neuroscience
No description supplie
Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance
Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario
A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor
Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach
Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation
In this paper a new method called SCLA which stands for Spiking based
Cellular Learning Automata is proposed for a mobile robot to get to the target
from any random initial point. The proposed method is a result of the
integration of both cellular automata and spiking neural networks. The
environment consists of multiple squares of the same size and the robot only
observes the neighboring squares of its current square. It should be stated
that the robot only moves either up and down or right and left. The environment
returns feedback to the learning automata to optimize its decision making in
the next steps resulting in cellular automata training. Simultaneously a
spiking neural network is trained to implement long term improvements and
reductions on the paths. The results show that the integration of both cellular
automata and spiking neural network ends up in reinforcing the proper paths and
training time reduction at the same time
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Artificial Neural Networks (ANNs) are currently being used as function
approximators in many state-of-the-art Reinforcement Learning (RL) algorithms.
Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy
consumption of ANNs by encoding information in sparse temporal binary spike
streams, hence emulating the communication mechanism of biological neurons. Due
to their low energy consumption, SNNs are considered to be important candidates
as co-processors to be implemented in mobile devices. In this work, the use of
SNNs as stochastic policies is explored under an energy-efficient
first-to-spike action rule, whereby the action taken by the RL agent is
determined by the occurrence of the first spike among the output neurons. A
policy gradient-based algorithm is derived considering a Generalized Linear
Model (GLM) for spiking neurons. Experimental results demonstrate the
capability of online trained SNNs as stochastic policies to gracefully trade
energy consumption, as measured by the number of spikes, and control
performance. Significant gains are shown as compared to the standard approach
of converting an offline trained ANN into an SNN.Comment: Submitted for conference publicatio
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
The dynamic membrane potential threshold, as one of the essential properties
of a biological neuron, is a spontaneous regulation mechanism that maintains
neuronal homeostasis, i.e., the constant overall spiking firing rate of a
neuron. As such, the neuron firing rate is regulated by a dynamic spiking
threshold, which has been extensively studied in biology. Existing work in the
machine learning community does not employ bioinspired spiking threshold
schemes. This work aims at bridging this gap by introducing a novel bioinspired
dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks
(SNNs). The proposed BDETT scheme mirrors two bioplausible observations: a
dynamic threshold has 1) a positive correlation with the average membrane
potential and 2) a negative correlation with the preceding rate of
depolarization. We validate the effectiveness of the proposed BDETT on robot
obstacle avoidance and continuous control tasks under both normal conditions
and various degraded conditions, including noisy observations, weights, and
dynamic environments. We find that the BDETT outperforms existing static and
heuristic threshold approaches by significant margins in all tested conditions,
and we confirm that the proposed bioinspired dynamic threshold scheme offers
homeostasis to SNNs in complex real-world tasks
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