Challenges in Data Management in Robotics Workshop
The Workshop on Challenges in Data Management in Robotics aims to bring together robotics enthusiasts, researchers, and industry professionals to discuss and address the pressing issues surrounding data management in robotics applications. Through keynotes, case studies, and breakout groups, participants will gain valuable insights into tackling data-related challenges in robotics and explore potential solutions.
Organized by IB Robotics and IB Artificial Intelligence
Official Website and Registration
Agenda (subject to change!)
| 08:30 – 09:00: | Registration and Welcome Coffee |
| 09:00 – 09:30: | Opening Remarks by Swiss Cobotics Competence Center and Innovation Boosters Robotics and Artificial Intelligence. |
| 09:30 – 10:30: | Presentation of the challenges |
Challenge 1: Coworking and Learning |
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| Understanding human practices within the context of human-robot interaction is crucial for robots to adjust their behaviors appropriately. However, this presents several challenges. Not only do technical tasks and ethical considerations come into play, but also the fact that different individuals interact in unique ways, making the development of universally applicable algorithms extremely difficult. Furthermore, ensuring precise communication and task allocation in the human-robot workflow is essential, especially considering the proximity between humans and robots. | |
Challenge 2: Data Management |
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| Most robots work with both filtered and uncensored data, not only for robot training and learning tasks but also directly acquired in-process by physical sensors. Nowadays, tasks such as training may require a large amount of data. This can be challenging in terms of data overload, costs of data analytics integration, and robust data management practices. |
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| 10:30 – 10:45: | Coffee Break |
| 10:45 – 12:15: | Breakout groups |
| 12:15 – 13:45: | Networking Lunch |
| 13:45 – 14:45: | Presentation of the challenges |
Challenge 3: Responsibe Collaboration |
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| Both humans and machines occasionally fail at specific tasks, making it essential to consider failure scenarios in developing robust interaction frameworks. By understanding and leveraging failure data, we aim to improve the design, development, control, and robustness robots, ensuring more resilient and adaptive human-robot interactions. To achieve this, we will explore methods for identifying and capturing failure events, analyzing their causes, and utilizing this data to improve the reliability and performance of robotic systems. | |
Challenge 4: Self-improvement of Robots |
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| One aspect of robustness involves the capacity for a robot to enhance its knowledge and behavior autonomously. This means it should possess a degree of flexibility to adapt and apply abilities to new situations as needed. Achieving this context-driven, adaptive autonomy, which relies on common-sense knowledge and practical manipulation tasks, demands extensive programming and often involves on-platform data management & analytics. |
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| 14:45 – 15:00: | Coffee Break |
| 15:00 – 16:30: | Breakout groups |
| 16:30 – 17:00: | Conclusions: Sharing Discussions and Findings |
