Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Artificial intelligence for healthcare and rescuing technology: technical developments and thoughts about employment impacts

Introduction: To evaluate the overall impact of Artificial Intelligence (AI) and Robotics on employment and work organization is complicated by the fact that these technologies are expected to revolutionize many application fields, which are very different from each other. In this paper, we consider two specific applications emerging from recent research projects: one applies AI and Robotics technologies to the healthcare sector, and one to Search and Rescue in wilderness areas. We generalize from these case studies to speculate on how this kind of innovative applications, that are likely to become increasingly common and widespread, might impact employment and work organization in general. Objectives: To understand how innovative applications might impact employment and work organization in general and specifically on healthcare and social services. Methods: Two recent research developments based on the use of Artificial Intelligence (AI) in the fields of healthcare and rescuing, respectively, are discussed. Therefore, our research work and main results have been achieved within a Swiss National Science Foundation project and a simplified view of the innovative classification component of the architecture is presented. Results: AI and Robotics technologies have specific application on healthcare and social services and demand new professional skills to manage those new methods. Conclusions: We conclude that, depending on the application field, a reduction in the workforce required to carry out tasks that will be taken over by automation might be counterbalanced by either a drastic increase in demand (healthcare services), or a shift in the required competences/skills (search and rescue); in both cases, we can expect a positive societal impact, also motivated by an increased standard of service.info:eu-repo/semantics/publishedVersio

Demo: Pointing Gestures for Proximity Interaction

We demonstrate a system to control robots in the users proximity with pointing gestures-a natural device that people use all the time to communicate with each other. Our setup consists of a miniature quadrotor Crazyflie 2.0, a wearable inertial measurement unit MetaWearR+ mounted on the user's wrist, and a laptop as the ground control station. The video of this demo is available at https://youtu.be/yafy-HZMk_U [1]

Learning Vision-Based Quadrotor Control in User Proximity

We consider a quadrotor equipped with a forward-facing camera, and an user freely moving in its proximity; we control the quadrotor in order to stay in front of the user, using only camera frames. To do so, we train a deep neural network to predict the drone controls given the camera image. Training data is acquired by running a simple hand-designed controller which relies on optical motion tracking data

Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution

A machine learning approach to visual perception of forest trails for mobile robots

We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle