Decoupled Learning of Environment Characteristics for Safe Exploration

Reinforcement learning is a proven technique for an agent to learn a task. However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task. This makes it harder to transfer skills between tasks in the same environment. Furthermore, this does not reduce risk when training for a new task. In this paper, we introduce an approach to decouple the environment characteristics from the task-specific ones, allowing an agent to develop a sense of survival. We evaluate our approach in an environment where an agent must learn a sequence of collection tasks, and show that decoupled learning allows for a safer utilization of prior knowledge.Comment: 4 pages, 4 figures, ICML 2017 workshop on Reliable Machine Learning in the Wil

Learning to Grasp from a single demonstration

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.Comment: 10 pages, 5 figures, IAS-15 2018 workshop on Learning Applications for Intelligent Autonomous Robot

Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

Internet of Things (IoT) infrastructures are more and more relying on multimedia sensors to provide information about the environment. Deep neural networks (DNNs) could extract knowledge from this audiovisual data but they typically require large amounts of resources (processing power, memory and energy). If all limitations of the execution environment are known beforehand, we can design neural networks under these constraints. An IoT setting however is a very heterogeneous environment where the constraints can change rapidly. We propose a technique allowing us to deploy a variety of different networks at runtime, each with a specific complexity-accuracy trade-off but without having to store each network independently. We train a sequence of networks of increasing size and constrain each network to contain the parameters of all smaller networks in the sequence. We only need to store the largest network to be able to deploy each of the smaller networks. We experimentally validate our approach on different benchmark datasets for image recognition and conclude that we can build networks that support multiple trade-offs between accuracy and computational cost. (C) 2019 Elsevier B.V. All rights reserved

Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Modern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification-a real-world use case-and show that this yields promising results

Learning to grasp arbitrary household objects from a single demonstration

Upon the advent of Industry 4.0, collaborative robotics and intelligent automation gain more and more traction for enterprises to improve their production processes. In order to adapt to this trend, new programming, learning and collaborative techniques are investigated. Program-by-demonstration is one of the techniques that aim to reduce the burden of manually programming collaborative robots. However, this is often limited to teaching to grasp at a certain position, rather than grasping a certain object. In this paper, we propose a method that learns to grasp an arbitrary object from visual input. While other learning-based approaches for robotic grasping require collecting a large dataset, manually or automatically labeled in a real or simulated world, our approach requires a single demonstration. We present results on grasping various objects with the Franka Panda collaborative robot after capturing a single image from a wrist mounted RGB camera. From this image we learn a robot controller with a convolutional neural network to adapt to changes in the object's position and rotation with less than 5 minutes of training time on a NVIDIA Titan X GPU, achieving over 90% grasp success rate