Uploaded January 2021 | Updated September 2026, 28 minutes ago
For more information about our SoftBank Robotics visit softbankrobotics.com/emea/en
Credits by order of appearance:
- FunDaLogic, USA fundalogic.com
- Simon Pierro simonpierro.com/robot
- Weegree, Poland weegreeone.com/en
- M6 Channel, E=m6
- DDTlab-RUK and Tilen Artač, Slovenia https://www.mcruk.si/en/
- Entrance Robotics GmbH in cooperation with the Seniorenzentrum Plettenberg, Germany entrance-robotics.de
- Welbo / Welkom! Netherlands https://www.welbo.eu/
- Behaviour Labs s.r.l.s. In collaboration with Casa Sollievo della Sofferenza, Italy https://www.blabs.eu/
- Pepper Parlor Café, Japan pepperparlor.com/en
- The English High School, Boston Public Schools
- Singh, A. K., Baranwal, N., Richter, K., Hellström, T., & Bensch, S. (2021). Verbal explanations by collaborating robot teams, Paladyn, Journal of Behavioral Robotics, 12(1), 47-57. doi: degruyter.com/view/journals/pjbr/12/1/article-p47.xml
- "Understandable Teams of Pepper Robots" Singh A.K., Baranwal N., Richter KF., Hellström T., Bensch S. (2020) Understandable Teams of Pepper Robots. In: Demazeau Y., Holvoet T., Corchado J., Costantini S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science, vol 12092. Springer, Cham. doi.org/10.1007/978-3-030-49778-1_43
- NISKA, Australia https://niska.com.au/
- “Dog Sit! Domestic Dogs (Canis familiaris) Follow a Robot’s Sit Commands,” by Meiying Qin, Yiyun Huang, Ellen Stumph, Laurie Santos, and Brian Scassellati from Yale University. Presented at HRI 2020. dl.acm.org/doi/abs/10.1145/3371382.3380734
- Caresses project, University of Genova, University of Bedfordshire and Advinia Healthcare, caressesrobot.org/en/project
- La main à la pâte, NAO@School projet, Middle School Ecole Georges Charpak, France
For more information about our SoftBank Robotics visit softbankrobotics.com/emea/en
Credits by order of appearance:
- FunDaLogic, USA fundalogic.com
- Simon Pierro simonpierro.com/robot
- Weegree, Poland weegreeone.com/en
- M6 Channel, E=m6
- DDTlab-RUK and Tilen Artač, Slovenia https://www.mcruk.si/en/
- Entrance Robotics GmbH in cooperation with the Seniorenzentrum Plettenberg, Germany entrance-robotics.de
- Welbo / Welkom! Netherlands https://www.welbo.eu/
- Behaviour Labs s.r.l.s. In collaboration with Casa Sollievo della Sofferenza, Italy https://www.blabs.eu/
- Pepper Parlor Café, Japan pepperparlor.com/en
- The English High School, Boston Public Schools
- Singh, A. K., Baranwal, N., Richter, K., Hellström, T., & Bensch, S. (2021). Verbal explanations by collaborating robot teams, Paladyn, Journal of Behavioral Robotics, 12(1), 47-57. doi: degruyter.com/view/journals/pjbr/12/1/article-p47.xml
- "Understandable Teams of Pepper Robots" Singh A.K., Baranwal N., Richter KF., Hellström T., Bensch S. (2020) Understandable Teams of Pepper Robots. In: Demazeau Y., Holvoet T., Corchado J., Costantini S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science, vol 12092. Springer, Cham. doi.org/10.1007/978-3-030-49778-1_43
- NISKA, Australia https://niska.com.au/
- “Dog Sit! Domestic Dogs (Canis familiaris) Follow a Robot’s Sit Commands,” by Meiying Qin, Yiyun Huang, Ellen Stumph, Laurie Santos, and Brian Scassellati from Yale University. Presented at HRI 2020. dl.acm.org/doi/abs/10.1145/3371382.3380734
- Caresses project, University of Genova, University of Bedfordshire and Advinia Healthcare, caressesrobot.org/en/project
- La main à la pâte, NAO@School projet, Middle School Ecole Georges Charpak, France
![[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.")
