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An ad-hoc ‘technology-driven’ creativity method
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| Cécile Boulard, Olivier Beorchia, Sophie Zijp-Rouzier |
| 20th Congress of the International Ergonomics Association (IEA), Florence, Italy, 26-30 August, 2018 |
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@inproceedings{boulard2018ad,
title={An Ad-Hoc ‘Technology-Driven’Creativity Method},
author={Boulard-Masson, C{\'e}cile and Zijp-Rouzier, Sophie and Beorchia, Olivier},
booktitle={Congress of the International Ergonomics Association},
pages={526--532},
year={2018},
organization={Springer}
}In innovation, many actors look for ways to generate new ideas i.e. to support creativity. In this paper we present a work conducted in a project that is “technol-ogy-driven”. A new haptic technology is in search of a context of use. As ergon-omists and designers we tried two methodologies to support ideation. The first one is a rather classical creativity session in a ‘focus group’. The second one is ‘technology-driven’ as it is first based on the experience with the prototype. The technology-driven methodology is divided in two steps. In the first one the partic-ipants experience the new haptic technology and try to imagine relevant use cases. The second step is another interview occurring a couple of days after the first one, where the participants report if they found any new use case between the two interviews. The conclusions show that both methodologies are very comple-mentary. In the creativity session, the quantity of produced use cases is higher and more ‘out of the box’. In the technology-driven, the ideas of use-case are more aligned with the haptic technology.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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