Uploaded September 2020 | Updated September 2026, 1 hour ago
Discover Pepper's improved navigation features!
By creating a visual map, Pepper can memorize points of reference which allow him to move from one point to another in full autonomy. All you have to do is showing him the path once!
Discover how Pepper can help you with your business: softbankrobotics.com/emea/en
#Retail #navigation #Covid19 #robotics #pepperrobot #deeplearning #Crowdbot
Discover Pepper's improved navigation features!
By creating a visual map, Pepper can memorize points of reference which allow him to move from one point to another in full autonomy. All you have to do is showing him the path once!
Discover how Pepper can help you with your business: softbankrobotics.com/emea/en
#Retail #navigation #Covid19 #robotics #pepperrobot #deeplearning #Crowdbot
![[AI Lab] - Pepper learning Velcro Darts](https://i.ytimg.com/vi/i_JBRojCqcc/mqdefault.jpg "[AI Lab] - Pepper learning Velcro Darts
After our previous video showing how Pepper the Robot learns to play the ball-in-a-cup game (https://www.youtube.com/watch?v=jkaRO8J_1XI), realized by the AI Lab of SoftBank Robotics, here we see how Pepper learns to play Velcro Darts in the same way.
The movement is first demonstrated to the robot by guiding its arm. From there, Pepper has to improve its performance through trial-and-error learning. Even though the initial demonstration does not hit the center of the dartboard, Pepper can still learn to play the game successfully.
After 60 trials, Pepper scores 100 points, most of the times!
The movement is represented as a so-called dynamic movement primitive and optimized using an evolutionary algorithm. Our implementation uses the freely available software library dmpbbo: https://github.com/stulp/dmpbbo.")
![[AI Lab] Pepper robot learning ball in a cup](https://i.ytimg.com/vi/jkaRO8J_1XI/mqdefault.jpg "[AI Lab] Pepper robot learning ball in a cup
This video realized by the AI Lab of SoftBank Robotics shows how Pepper robot learns to play the ball-in-a-cup game (bilboquet in French). The movement is first demonstrated to the robot by guiding its arm.
From there, Pepper has to improve its performance through trial-and-error learning. Even though the initial demonstration does not land the ball in the cup, Pepper can still learn to play the game successfully.
The movement is represented as a so-called dynamic movement primitive and optimized using an evolutionary algorithm. Our implementation uses the freely available software library dmpbbo: https://github.com/stulp/dmpbbo.
After 100 trials, Pepper has successfully optimized its behavior and is able to repeatedly land the ball in the cup.")
