13 Items found
An international partnership where SBRE puts forward Pepper to answer user specific needs - How can technology support the worldwide issue of the ageing populations and the complexity of human connections? SBRE committed into the CARESSES program to integrate a system that plans the robot's actions according to a relevant cultural knowledge base.
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.
How to use logs in Android Studio to develop QiSDK application on Pepper robot - The aim of this tutorial is to give a quick overview of logging in the Android Studio environment. As a live example to put logs into practice, we will also focus on the application lifecycle in the QiSDK context which is based on Android lifecycle.
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.
Sending Slack messages by voice thanks to Pepper - What if Pepper could help you increase your productivity at work? By linking the robot to a Slack channel, we will be able to send and receive messages by voice control thanks to Pepper.
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.