5 Items found
Blog

qiBullet

qiBullet
a Bullet-based simulator for the robots - qiBullet is a simulation tool enabling users to easily experiment with Pepper and NAO robots in virtual environments and gather accurate data.
2020/02/27
Pepper closeup
Pepper (NAOqi 2.5)
NAO closeup
NAO⁶
qiBullet
Blog

CROWDBOT: Safe Navigation of Robots in Dense Crowds.

CROWDBOT: Safe Navigation of Robots in Dense Crowds.
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.
2020/05/26
Pepper closeup
Pepper QiSDK
Pepper closeup
Pepper (NAOqi 2.5)
NAO closeup
NAO⁶
CROWDBOT: Safe Navigation of Robots in Dense Crowds.
Blog

Developer Center v1 Release note ("Opening Night")

Developer Center v1 Release note ("Opening Night")
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.
2019/05/17
Pepper closeup
Pepper QiSDK
Pepper closeup
Pepper (NAOqi 2.5)
NAO closeup
NAO⁶
Developer Center v1 Release note ("Opening Night")
Blog

Controlling Robots with Human Poses

Controlling Robots with Human Poses
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.
2020/12/31
Pepper closeup
Pepper QiSDK
Pepper closeup
Pepper (NAOqi 2.5)
NAO closeup
NAO⁶
Controlling Robots with Human Poses
Blog

Learning a problem representation while exploring it

Learning a problem representation while exploring it
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.
2021/02/24
Pepper closeup
Pepper QiSDK
Pepper closeup
Pepper (NAOqi 2.5)
NAO closeup
NAO⁶
Learning a problem representation while exploring it