Blending reality with simulation:…Gibson environment trains robots with systems and embodiment designed to better map to real world data…Researchers with Stanford University and the University of California at Berkeley have created Gibson, an environment for teaching agents to learn to navigate spaces. Gibson is one of numerous navigation environments available to modern researchers and its distinguishing characteristics include: basing the environments on real spaces, and some clever rendering techniques to ensure that images seen by agents within Gibson more closely match real world images by “embedding a mechanism to dissolve differences between Gibson’s renderings and what a real camera would produce”. Scale: “Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings,” they write. The researchers also compare the total size of the Gibson dataset to other large-scale environment datasets including ‘SUNCG’ and Matterport3D, showing that Gibson has reasonable navigation complexity and a lower real-world transfer error than other systems.** Data gathering:** The researchers use a variety of different scanning devices to gather the data for Gibson, including NavVis, Matterport, and Dotproduct. Experiments: So how useful is Gibson? The researchers perform several experiments to evaluate its effectiveness. These include experiments around local planning and obstacle avoidance; distant visual navigation; and climbing stairs, as well as transfer learning experiments that measure the depth estimation and scene classification capabilities of the system .** Limitations:** Gibson has a few limitations, which include a lack of support for dynamic content (such as other moving objects) as well as no support for manipulation of the environment around itself. Future tests will involve testing if Gibson can work on finished robots as well. Read more: Gibson Env: Real-World Perception for Embodied Agents (Arxiv). Find out more: Gibson official website.** Gibson on GitHub.**
Get ready for medieval graffiti:…4,000 images, some older than a thousand years, from an Eastern European church…Researchers with the National Technical University of Ukraine have created a dataset of images of medieval graffiti written in two alphabets (Glagolitic and Cyrillic) on the St. Sophia Cathedral of Kiev in the Ukraine, providing researchers with a dataset they can use to train and develop supervised and unsupervised classification and generation systems. Dataset: The researchers created a dataset of Carved Glagolitic and Crillic letters (CGCL), consisting of more than 4,000 images of 34 types of letters.** Why it matters: One of the more remarkable aspects of basic supervised learning is that given sufficient data it becomes relatively easy to automate the perception of something in the world – further digitization of datasets like these increases the likelihood that in the future we’ll use drones or robots to automatically scan ancient buildings across the world, identifying and transcribing thoughts inscribed hundreds or thousands of years ago. Graffiti never dies! Read more:** Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti (Arxiv).