8 Items found
CROWDBOT, an European Collaborative project enables robots to freely navigate and assist humans in crowded areas. - Today’s moving robots stop when a human, or any obstacle is too close, to avoid impact. This prevents robots from entering packed areas and effectively performing in a high dynamic environment. CROWDBOT aims to fill in the gap in knowledge on close interactions between robots and humans in motion.
A Reader's Digest about the NAOqi Release Note and relevant information for partners and developers. - NAOqi is the robot’s Operating System, the main software that runs on the robot and controls it. This public Release Note intends to give a brief review of the last notable changes impacting developers.
We previously released a preview of the Developer Center website (“Early birds”) in April and we release its first public version (“Opening Night”) on May the 16h. We do thank you for your reactions and feedback which was taken into account and helps us improve the developer experience.
How to install a language package and get speech recognition in new languages - As international talking robots, Pepper and NAO can access many languages for speech recognition, and its management is quite easy. This quick how-to explains how to set up new languages installing a language pack in a few simple steps.
A Research project about Pepper and NAO immersive teleoperation - A new immersive teleoperation solution based on Extreme Learning Machine (ELM), a Machine Learning technique, is introduced for Pepper and NAO robots. Immersive teleoperation is defined as a robot remote control that renders the operator the sensation of being inside the teleoperated environment and providing a feeling of being the robot itself. The solution is independent of other mapping approaches (e.g. inverse kinematics) and the user’s whole body is used for the robot control. Even with scarce training data, the solution returns satisfactory results in both precision and computational speed.
Learning algorithms generally need significant prior information on the task they are attempting to solve. This requirement limits their flexibility and forces engineers to provide appropriate priors at design time. A question then arises naturally: how can we reduce the amount of prior task information needed by the algorithm? Being able to answer this question could spark the development of algorithms with higher generalization potential, all the while reducing preliminary engineering efforts. If we want to discard any a priori knowledge about the task, we need the learning algorithm to efficiently explore and represent the space of possible outcomes it can achieve.