53 research outputs found
EASpace: Enhanced Action Space for Policy Transfer
Formulating expert policies as macro actions promises to alleviate the
long-horizon issue via structured exploration and efficient credit assignment.
However, traditional option-based multi-policy transfer methods suffer from
inefficient exploration of macro action's length and insufficient exploitation
of useful long-duration macro actions. In this paper, a novel algorithm named
EASpace (Enhanced Action Space) is proposed, which formulates macro actions in
an alternative form to accelerate the learning process using multiple available
sub-optimal expert policies. Specifically, EASpace formulates each expert
policy into multiple macro actions with different execution {times}. All the
macro actions are then integrated into the primitive action space directly. An
intrinsic reward, which is proportional to the execution time of macro actions,
is introduced to encourage the exploitation of useful macro actions. The
corresponding learning rule that is similar to Intra-option Q-learning is
employed to improve the data efficiency. Theoretical analysis is presented to
show the convergence of the proposed learning rule. The efficiency of EASpace
is illustrated by a grid-based game and a multi-agent pursuit problem. The
proposed algorithm is also implemented in physical systems to validate its
effectiveness.Comment: 15 Page
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments
Neuromorphic vision sensors (event cameras) are inherently suitable for
spiking neural networks (SNNs) and provide novel neuromorphic vision data for
this biomimetic model. Due to the spatiotemporal characteristics, novel data
augmentations are required to process the unconventional visual signals of
these cameras. In this paper, we propose a novel Event SpatioTemporal Fragments
(ESTF) augmentation method. It preserves the continuity of neuromorphic data by
drifting or inverting fragments of the spatiotemporal event stream to simulate
the disturbance of brightness variations, leading to more robust spiking neural
networks. Extensive experiments are performed on prevailing neuromorphic
datasets. It turns out that ESTF provides substantial improvements over pure
geometric transformations and outperforms other event data augmentation
methods. It is worth noting that the SNNs with ESTF achieve the
state-of-the-art accuracy of 83.9\% on the CIFAR10-DVS dataset.Comment: Accepted by ICASSP 202
Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons
As the third generation of neural networks, spiking neural networks (SNNs)
are dedicated to exploring more insightful neural mechanisms to achieve
near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to
understanding and improving SNNs. For example, the associative long-term
potentiation (ALTP) phenomenon suggests that in addition to learning mechanisms
between neurons, there are associative effects within neurons. However, most
existing methods only focus on the former and lack exploration of the internal
association effects. In this paper, we propose a novel Adaptive Internal
Association~(AIA) neuron model to establish previously ignored influences
within neurons. Consistent with the ALTP phenomenon, the AIA neuron model is
adaptive to input stimuli, and internal associative learning occurs only when
both dendrites are stimulated at the same time. In addition, we employ weighted
weights to measure internal associations and introduce intermediate caches to
reduce the volatility of associations. Extensive experiments on prevailing
neuromorphic datasets show that the proposed method can potentiate or depress
the firing of spikes more specifically, resulting in better performance with
fewer spikes. It is worth noting that without adding any parameters at
inference, the AIA model achieves state-of-the-art performance on
DVS-CIFAR10~(83.9\%) and N-CARS~(95.64\%) datasets.Comment: Accepted by ICASSP 202
Collaborative Target Tracking in Elliptic Coordinates: a Binocular Coordination Approach
This paper concentrates on the collaborative target tracking control of a
pair of tracking vehicles with formation constraints. The proposed controller
requires only distance measurements between tracking vehicles and the target.
Its novelty lies in two aspects: 1) the elliptic coordinates are used to
represent an arbitrary tracking formation without singularity, which can be
deduced from inter-agent distances, and 2) the regulation of the tracking
vehicle system obeys a binocular coordination principle, which simplifies the
design of the control law by leveraging rich physical meanings of elliptic
coordinates. The tracking system with the proposed controller is proven to be
exponentially convergent when the target is stationary. When the target drifts
with a small velocity, the desired tracking formation is achieved within a
small margin proportional to the magnitude of the target's drift velocity.
Simulation examples are provided to demonstrate the tracking performance of the
proposed controller.Comment: 6 pages, 5 figure
Triangular lattice formation in robot swarms with minimal local sensing
Abstract The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi‐robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi‐robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots
Experimental and analytical study on factors influencing biomimetic undulating fin propulsion performance based on orthogonal experimental design
This paper presents an experimental study on the structural and motion parameters of a biomimetic mechanical fin, as actuators for biomimetic underwater vehicles, and their influences on its propulsion performance. Orthogonal experimental design method is employed to optimize the test scheme due to its capability of reducing experiment time and rapid determination of influencing factors. A L64 orthogonal array is adopted in our seven-factor mixed factorial experiment with three structure parameters and four motion parameters. The experimental results of range analysis and variance analysis are discussed. The statistic results show that the fin-ray configuration, sway frequency, wave number and rigidity of membrane are the primary factors affecting the integrated propelling performance of the biomimetic mechanical fin. Optimizing these parameters needs to compromise the propelling speed, efficiency and maximum output power of the actuator. The analytic results are also in agreement with theoretical analysis and simulation study on inclined-angle of fish fin-ray. Consequently, proper modification of the inclined angle of the fin-ray might improve not only the propelling speed and acceleration, but also the propelling efficiency
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