![[AI Lab] - Pepper learning Velcro Darts @MaxtronicsRobotics](https://i.ytimg.com/vi/i_JBRojCqcc/maxresdefault.jpg)
Uploaded December 2016 | Updated September 2026, 1 hour ago
After our previous video showing how Pepper the Robot learns to play the ball-in-a-cup game (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: github.com/stulp/dmpbbo.
After our previous video showing how Pepper the Robot learns to play the ball-in-a-cup game (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: github.com/stulp/dmpbbo.
![[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.")
![[Atalk] Benjamin Kuipers - How can we trust a robot? 2/2](https://i.ytimg.com/vi/mM5xz2CYPUY/mqdefault.jpg "[Atalk] Benjamin Kuipers - How can we trust a robot? 2/2
Benjamin Kuipers from the University of Michigan is asking us a simple but nevertheless essential question: how can we trust a robot?
We are increasingly seeing robots and other AIs that perceive and respond to the complexities of the human environment, making decisions about how it is appropriate to act in the current situation. In effect, they are functioning as members of our society. They drive cars autonomously on our roads, they help care for children and the elderly, and they operate complex distributed systems in the infrastructures of our world. How can we design a robot to be trustworthy? How can we verify its trustworthiness?
How does a robot decide what to do? The standard notion of rationality in artificial intelligence, derived from game theory, says that a rational agent should choose the action that maximizes its expected utility. In principle, utility can be very sophisticated, but in practice, it is typically defined as the agents own reward. Unfortunately, scenarios like the Tragedy of the Commons and the Prisoners Dilemma show that self-interested reward-maximization can easily lead to very poor outcomes both for the individual and for society. (Fictional and non-fictional scenarios demonstrate that these problems are quite realistic.)
Trust is a critical foundation for the non-zero-sum cooperative activities that allow society, and the individuals within it, to thrive. In order to build robots that function well in society, we need to formalize trust. We draw on classical theories in philosophical ethics, and on recent progress in the cognitive sciences, to understand the roles of trust, morality, and ethics in human society. We examine efforts to express trust within the formalism of game theory, and outline a research agenda for the future.
About Benjamin Kuipers:
Benjamin Kuipers joined the University of Michigan in January 2009 as Professor of Computer Science and Engineering. Prior to that, he held an endowed Professorship in Computer Sciences at the University of Texas at Austin. He received his B.A. from Swarthmore College, and his Ph.D. from MIT. He investigates the representation of commonsense and expert knowledge, with particular emphasis on the effective use of incomplete knowledge. His research accomplishments include developing the TOUR model of spatial knowledge in the cognitive map, the QSIM algorithm for qualitative simulation, the Algernon system for knowledge representation, and the Spatial Semantic Hierarchy model of knowledge for robot exploration and mapping. He has served as Department Chair at UT Austin, and is a Fellow of AAAI, IEEE, and AAAS.")
