• Outdoor positioning for the IoT world

    How can I get an estimate of my position using a mobile device? The answer to this question would be quite complex a few decades ago. On the other hand, the spontaneous answer of most people nowadays would be “using the GPS on my phone” (or on some other mobile device). The use of GPS and other satellite systems, such as the European Galileo, the Russian GLONASS, or China’s BeiDou, have familiarized the broad public with the concept of outdoor positioning, which is often considered a solved problem. During the last few years though, the emergence of Internet of Things (IoT) technologies is bringing to the market and to our daily lives a plethora of small, low power devices. These devices ...

    Read more
  • Lifelong Generative Modeling

    The case for lifelong learning Lifelong learning (also known as continual learning) is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning . ...

    Read more
  • Sample-Efficient Imitation Learning via Generative Adversarial Nets Sample-Efficient Imitation Learning via Generative Adversarial Nets

    Generative Adversarial Imitation Learning (GAIL) . Albeit successful at generating behaviours similar to those demonstrated to the agent, GAIL suffers from a high sample complexity in the number of interactions it has to carry out in the environment in order to achieve satisfactory performance. We dramatically shrink the amount of interactions with the environment necessary to learn well-behaved imitation policies, by up to several orders of magnitude. Our framework, operating in the model-free regime, exhibits a significant increase in sample-efficiency over previous methods by simultaneously a) learning a self-tuned adversarially-trained surrogate reward and b) leveraging an off-policy actor-critic architecture. We ...

    Read more