State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion. This causes MR-IG algorithms to fail when they are applied to new IG tasks, as existing models cannot describe the information of interest. Therefore, we propose in this paper a MR-IG algorithm that can be applied to new IG tasks with little algorithmic changes. To this end, we introduce DeepIG: a MR-IG algorithm that uses Deep Reinforcement Learning to allow robots to learn how to gather information. Nevertheless, there are IG tasks for which accurate models have been derived. Therefore, we extend DeepIG to exploit existing models for such IG tasks. This algorithm we term it model-based DeepIG (MB-DeepIG). First, we evaluate DeepIG in simulations, and in an indoor experiment with three quadcopters that autonomously map an unknown terrain profile built in our lab. Results demonstrate that DeepIG can be applied to different IG tasks without algorithmic changes, and that it is robust to measurement noise. Then, we benchmark MB-DeepIG against state-of-the-art informationdriven Gaussian-processes-based IG algorithms. Results demonstrate that MB-DeepIG outperforms the considered benchmarks.

A range of new technologies have the potential to help people, whether traditionally considered hearing impaired or not. These technologies include more sophisticated personal sound amplification products, as well as real-time speech enhancement and speech recognition. They can improve user’s communication abilities, but these new approaches require new ways to describe their success and allow engineers to optimize their properties. Speech recognition systems are often optimized using the word-error rate, but when the results are presented in real time, user interface issues become a lot more important than conventional measures of auditory performance. For example, there is a tradeoff between minimizing recognition time (latency) by quickly displaying results versus disturbing the user’s cognitive flow by rewriting the results on the screen when the recognizer later needs to change its decisions. This article describes current, new, and future directions for helping billions of people with their hearing. These new technologies bring auditory assistance to new users, especially to those in areas of the world without access to professional medical expertise. In the short term, audio enhancement technologies in inexpensive mobile forms, devices that are quickly becoming necessary to navigate all aspects of our lives, can bring better audio signals to many people. Alternatively, current speech recognition technology may obviate the need for audio amplification or enhancement at all and could be useful for listeners with normal hearing or with hearing loss. With new and dramatically better technology based on deep neural networks, speech enhancement improves the signal to noise ratio, and audio classifiers can recognize sounds in the user’s environment. Both use deep neural networks to improve a user’s experiences. Longer term, auditory attention decoding is expected to allow our devices to understand where a user is directing their attention and thus allow our devices to respond better to their needs. In all these cases, the technologies turn the hearing assistance problem on its head, and thus require new ways to measure their performance.

Abstract. This paper proposes a modular and generic architecture to deal with Global Chassis Control. Reinforcement learning is coupled with intelligent PID controllers and an optimal tire effort allocation algorithm to obtain a general, robust, adaptable, efficient and safe control architecture for any kind of automated wheeled vehicle.

Memory neural networks exhibit promising results for use as adaptive controllers for systems involving nonlinear time-delayed dynamics [I]. By associating a memory neuron to each network neuron, we alleviate the requirement for storing and recurrently feeding a nonlinear plant's past histories in order to adaptively control the system. Past attempts at designing a missile controller with a memory neural network produced encouraging results for the estimation facet of system modeling. [6] This paper presents the design and simulation of a model reference adaptive controller for a missile system using memory neural networks.