Spillover Algorithm: A Decentralized Coordination Approach for Multi-Robot Production Planning in Open Shared Factories

Open and shared manufacturing factories typically dispose of a limited number of robots that should be properly allocated to tasks in time and space for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture and location of robots need to be determined at consecutive periods within a given time horizon and products can be anticipated or backordered related to the demand period. We consider a decentralized multi-agent variant of this problem in an open factory setting with multiple owners of robots as well as different owners of the items to be produced, both considered self-interested and individually rational. Existing solution approaches to the classic constrained lot-sizing problem are centralized exact methods that require sharing of global knowledge of all the participants' private and sensitive information and are not applicable in the described multi-agent context. Therefore, we propose a computationally efficient decentralized approach based on the spillover effect that solves this NP-hard problem by distributing decisions in an intrinsically decentralized multi-agent system environment while protecting private and sensitive information. To the best of our knowledge, this is the first decentralized algorithm for the solution of the studied problem in intrinsically decentralized environments where production resources and/or products are owned by multiple stakeholders with possibly conflicting objectives. To show its efficiency, the performance of the Spillover Algorithm is benchmarked against state-of-the-art commercial solver CPLEX 12.8.

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.

Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robot

In this paper, a hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit built by Agility Robotics. We propose a cascade-structure controller that combines the learning process with intuitive feedback regulations. This design allows the framework to realize robust and stable walking with a reduced-dimension state and action spaces of the policy, significantly simplifying the design and reducing the sampling efficiency of the learning method. The inclusion of feedback regulation into the framework improves the robustness of the learned walking gait and ensures the success of the sim-to-real transfer of the proposed controller with minimal tuning. We specifically present a learning pipeline that considers hardware-feasible initial poses of the robot within the learning process to ensure the initial state of the learning is replicated as close as possible to the initial state of the robot in hardware experiments. Finally, we demonstrate the feasibility of our method by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not included during the training process. To the best of our knowledge, this is the first time a learning-based policy is transferred successfully to the Digit robot in hardware experiments without using dynamic randomization or curriculum learning.

Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations

We present a neural network-based method for solving linear and nonlinear partial differential equations, by combining the ideas of extreme learning machines (ELM), domain decomposition and local neural networks. The field solution on each sub-domain is represented by a local feed-forward neural network, and $C^k$ continuity is imposed on the sub-domain boundaries. Each local neural network consists of a small number of hidden layers, while its last hidden layer can be wide. The weight/bias coefficients in all hidden layers of the local neural networks are pre-set to random values and are fixed, and only the weight coefficients in the output layers are training parameters. The overall neural network is trained by a linear or nonlinear least squares computation, not by the back-propagation type algorithms. We introduce a block time-marching scheme together with the presented method for long-time dynamic simulations. The current method exhibits a clear sense of convergence with respect to the degrees of freedom in the neural network. Its numerical errors typically decrease exponentially or nearly exponentially as the number of degrees of freedom increases. Extensive numerical experiments have been performed to demonstrate the computational performance of the presented method. We compare the current method with the deep Galerkin method (DGM) and the physics-informed neural network (PINN) in terms of the accuracy and computational cost. The current method exhibits a clear superiority, with its numerical errors and network training time considerably smaller (typically by orders of magnitude) than those of DGM and PINN. We also compare the current method with the classical finite element method (FEM). The computational performance of the current method is on par with, and oftentimes exceeds, the FEM performance.

Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication

How do adults understand children's speech? Children's productions over the course of language development often bear little resemblance to typical adult pronunciations, yet caregivers nonetheless reliably recover meaning from them. Here, we employ a suite of Bayesian models of spoken word recognition to understand how adults overcome the noisiness of child language, showing that communicative success between children and adults relies heavily on adult inferential processes. By evaluating competing models on phonetically-annotated corpora, we show that adults' recovered meanings are best predicted by prior expectations fitted specifically to the child language environment, rather than to typical adult-adult language. After quantifying the contribution of this "child-directed listening" over developmental time, we discuss the consequences for theories of language acquisition, as well as the implications for commonly-used methods for assessing children's linguistic proficiency.

Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions

The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical properties. In this study, a new methodology to manipulate elastic rods automatically into 2D desired shapes is presented. An efficient vision-based controller that uses a deep autoencoder network is designed to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features is used to deal with the latter's (typically unknown) mechanical properties. The proposed approach computes the rod's centerline from raw visual data in real-time by introducing an adaptive algorithm on the basis of a self-organizing network. Its effectiveness is thoroughly validated with simulations and experiments.

Needmining: Designing Digital Support to Elicit Needs from Social Media

Today's businesses face a high pressure to innovate in order to succeed in highly competitive markets. Successful innovations, though, typically require the identification and analysis of customer needs. While traditional, established need elicitation methods are time-proven and have demonstrated their capabilities to deliver valuable insights, they lack automation and scalability and, thus, are expensive and time-consuming. In this article, we propose an approach to automatically identify and quantify customer needs by utilizing a novel data source: Users voluntarily and publicly expose information about themselves via social media, as for instance Facebook or Twitter. These posts may contain valuable information about the needs, wants, and demands of their authors. We apply a Design Science Research (DSR) methodology to add design knowledge and artifacts for the digitalization of innovation processes, in particular to provide digital support for the elicitation of customer needs. We want to investigate whether automated, speedy, and scalable need elicitation from social media is feasible. We concentrate on Twitter as a data source and on e-mobility as an application domain. In a first design cycle we conceive, implement and evaluate a method to demonstrate the feasibility of identifying those social media posts that actually express customer needs. In a second cycle, we build on this artifact to additionally quantify the need information elicited, and prove its feasibility. Third, we integrate both developed methods into an end-user software artifact and test usability in an industrial use case. Thus, we add new methods for need elicitation to the body of knowledge, and introduce concrete tooling for innovation management in practice.

Artificial Intelligence based Sensor Data Analytics Framework for Remote Electricity Network Condition Monitoring

Rural electrification demands the use of inexpensive technologies such as single wire earth return (SWER) networks. There is a steadily growing energy demand from remote consumers, and the capacity of existing lines may become inadequate soon. Furthermore, high impedance arcing faults (HIF) from SWER lines can cause catastrophic bushfires such as the 2009 Black Saturday event. As a solution, reliable remote electricity networks can be established through breaking the existing systems down into microgrids, and existing SWER lines can be utilised to interconnect those microgrids. The development of such reliable networks with better energy demand management will rely on having an integrated network-wide condition monitoring system. As the first contribution of this thesis, a distributed online monitoring platform is developed that incorporates power quality monitoring, real-time HIF identification and transient classification in SWER network. Artificial Intelligence (AI) based techniques are developed to classify faults and transients. The proposed approach demonstrates higher HIF detection accuracy (98.67%) and reduced detection latency (115.2 ms). Secondly, a remote consumer load identification methodology is developed to detect the load type from its transients. An edge computing-based architecture is proposed to facilitate the high-frequency analysis for load identification. The proposed approach is evaluated in real-time, and it achieves an average accuracy of 98% in identifying different loads. Finally, a deep neural network-based energy disaggregation framework is developed to separate the load specific energy usage from an aggregated signal. The proposed framework is evaluated using a real-world data set. It improves the signal aggregate error by 44% and mean aggregate error by 19% in comparison with the state-of-the-art techniques.

MoViNets: Mobile Video Networks for Efficient Video Recognition

We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference. 3D convolutional neural networks (CNNs) are accurate at video recognition but require large computation and memory budgets and do not support online inference, making them difficult to work on mobile devices. We propose a three-step approach to improve computational efficiency while substantially reducing the peak memory usage of 3D CNNs. First, we design a video network search space and employ neural architecture search to generate efficient and diverse 3D CNN architectures. Second, we introduce the Stream Buffer technique that decouples memory from video clip duration, allowing 3D CNNs to embed arbitrary-length streaming video sequences for both training and inference with a small constant memory footprint. Third, we propose a simple ensembling technique to improve accuracy further without sacrificing efficiency. These three progressive techniques allow MoViNets to achieve state-of-the-art accuracy and efficiency on the Kinetics, Moments in Time, and Charades video action recognition datasets. For instance, MoViNet-A5-Stream achieves the same accuracy as X3D-XL on Kinetics 600 while requiring 80% fewer FLOPs and 65% less memory. Code will be made available at https://github.com/tensorflow/models/tree/master/official/vision.

Scatter Correction in X-ray CT by Physics-Inspired Deep Learning

A fundamental problem in X-ray Computed Tomography (CT) is the scatter due to interaction of photons with the imaged object. Unless corrected, scatter manifests itself as degradations in the reconstructions in the form of various artifacts. Scatter correction is therefore critical for reconstruction quality. Scatter correction methods can be divided into two categories: hardware-based; and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware, or increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical, and kernel-based methods were developed. Furthermore, data-driven approaches to tackle this problem were recently demonstrated. In this work, two novel physics-inspired deep-learning-based methods, PhILSCAT and OV-PhILSCAT, are proposed. The methods estimate and correct for the scatter in the acquired projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common deep neural network architecture and cost function, both tailored to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal significant improvement over a recent purely projection-domain deep neural network scatter correction method.