Uploaded October 2016 | Updated September 2026, 1 hour ago
Discover our Certified Pepper Partners, introducing a broad range of B2B applications and solutions developped for Pepper. These applications are answering several business needs in Retail, Banking, Hospitality or Healthcare.
Robotrónica’s Application will allow Pepper to advise or suggest to any customer which product is most suitable for them based on age, gender and mood or, at request by telling the name or just showing the box, Pepper will be able to inform about a specific article while the tablet displays all related available information.
In addition, Pepper will alsonprocess all collected data to build marketing reports and stats.
Discover our Certified Pepper Partners, introducing a broad range of B2B applications and solutions developped for Pepper. These applications are answering several business needs in Retail, Banking, Hospitality or Healthcare.
Robotrónica’s Application will allow Pepper to advise or suggest to any customer which product is most suitable for them based on age, gender and mood or, at request by telling the name or just showing the box, Pepper will be able to inform about a specific article while the tablet displays all related available information.
In addition, Pepper will alsonprocess all collected data to build marketing reports and stats.
![[Atalk] Oliver Brock - Variability : The main obstacle for general robotics? 2/2](https://i.ytimg.com/vi/Q7L08BNsjqg/mqdefault.jpg "[Atalk] Oliver Brock - Variability : The main obstacle for general robotics? 2/2
Abstract:
The biggest success story of robotics remains factory automation. But our field is struggling to repeat this success in any other domain, especially in domains we call unstructured. Among the main culprits for this difficulty have been uncertainty and the high-dimensionality of configuration spaces associated with versatile robots. In my talk, I will argue that our field has found successful solutions for uncertainty and high-dimensionality, as long as they pertain to the robot itself. I will argue that after alleviating these two problems, a new problem has become the next bottleneck: the variability of the environment. On our path towards general robotics, we must focus on handling this variability. This might seem completely obvious, but I believe the most important implications of this realization have been largely ignored. I will present some attempts to address the variability in the environment, including interactive perception and learning with robotic priors. I will also place these solutions in the context of other attempts to address variability, such as big data and deep learning.
Bio:
Oliver Brock is the Alexander-von-Humboldt Professor of Robotics in the School of Electrical Engineering and Computer Science at the Technische Universität Berlin in Germany. He received his Diploma in Computer Science in 1993 from the Technische Universität Berlin and his Masters and Ph.D. in Computer Science from Stanford University in 1994 and 2000, respectively. He also held post-doctoral positions at Rice University and Stanford University. Starting in 2002, he was an Assistant Professor and Associate Professor in the Department of Computer Science at the University of Massachusetts Amherst, before to moving back to the Technische Universität Berlin in 2009. The research of Brocks lab, the Robotics and Biology Laboratory, focuses on autonomous mobile manipulation, interactive perception, grasping, manipulation, soft hands, interactive learning, motion generation, and the application of algorithms and concepts from robotics to computational problems in structural molecular biology. He is also the president of the Robotics: Science and Systems foundation.")
![[AI Lab] Pepper Identifies obstacles](https://i.ytimg.com/vi/ReTvuCRQlq0/mqdefault.jpg "[AI Lab] Pepper Identifies obstacles
This video realized by the AI Lab at SoftBank Robotics Europe shows how state-of-the-art Deep Learning technologies can be applied to identify traversable terrain from Peppers RGB cameras.
Three approaches are evaluated: pixel-wise segmentation, pixel-wise depth estimation, and full image classification.
The first two approaches are based on off-the-shelf Deep Nets trained on the SUNRGBD and NYUv2 databases. They produce interesting results despite no fine-tuning to Peppers hardware.
The last approach uses a Deep Net adapted from ALEXNET and trained on a database built inside SBRE offices with Peppers cameras.
Each approach is successively evaluated by manually moving Pepper. Then autonomous displacements based on the full image classification are presented.")
