99,425 research outputs found
Online Learning of a Memory for Learning Rates
The promise of learning to learn for robotics rests on the hope that by
extracting some information about the learning process itself we can speed up
subsequent similar learning tasks. Here, we introduce a computationally
efficient online meta-learning algorithm that builds and optimizes a memory
model of the optimal learning rate landscape from previously observed gradient
behaviors. While performing task specific optimization, this memory of learning
rates predicts how to scale currently observed gradients. After applying the
gradient scaling our meta-learner updates its internal memory based on the
observed effect its prediction had. Our meta-learner can be combined with any
gradient-based optimizer, learns on the fly and can be transferred to new
optimization tasks. In our evaluations we show that our meta-learning algorithm
speeds up learning of MNIST classification and a variety of learning control
tasks, either in batch or online learning settings.Comment: accepted to ICRA 2018, code available:
https://github.com/fmeier/online-meta-learning ; video pitch available:
https://youtu.be/9PzQ25FPPO
Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design
Conventional meta-atom designs rely heavily on researchers' prior knowledge
and trial-and-error searches using full-wave simulations, resulting in
time-consuming and inefficient processes. Inverse design methods based on
optimization algorithms, such as evolutionary algorithms, and topological
optimizations, have been introduced to design metamaterials. However, none of
these algorithms are general enough to fulfill multi-objective tasks. Recently,
deep learning methods represented by Generative Adversarial Networks (GANs)
have been applied to inverse design of metamaterials, which can directly
generate high-degree-of-freedom meta-atoms based on S-parameter requirements.
However, the adversarial training process of GANs makes the network unstable
and results in high modeling costs. This paper proposes a novel metamaterial
inverse design method based on the diffusion probability theory. By learning
the Markov process that transforms the original structure into a Gaussian
distribution, the proposed method can gradually remove the noise starting from
the Gaussian distribution and generate new high-degree-of-freedom meta-atoms
that meet S-parameter conditions, which avoids the model instability introduced
by the adversarial training process of GANs and ensures more accurate and
high-quality generation results. Experiments have proven that our method is
superior to representative methods of GANs in terms of model convergence speed,
generation accuracy, and quality
Morphological properties of mass-spring networks for optimal locomotion learning
Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size
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