Abstract
Publications home
“I Don’t Understand…” issues in self-quantifying commuting
at
| Cécile Boulard, Stéfania Castellani, Tommaso Colombino, Antonietta Grasso |
| European Conference on Cognitive Ergonomics (ECCE), Belfast, Northern Ireland, 10-13 September, 2019 |
| Download |
@inproceedings{boulard2019don,
title={“I Don't Understand…” Issues in Self-Quantifying Commuting},
author={Boulard, Cecile and Castellani, Stefania and Colombino, Tommaso and Grasso, Antonietta},
booktitle={Proceedings of the 31st European Conference on Cognitive Ergonomics},
pages={144--147},
year={2019}
}
In the context of addressing global warming issues one of the possible approaches is to provide individuals with tools that support behavior change toward greener practices, as for example in commuting. This paper illustrates the results of a study that we conducted on the effectiveness of self-tracking of commuting data where participants received daily feedback on the financial costs and CO2 emissions associated to their mobility practices. In the results, we describe situations where users either misunderstood or did not accept the data and the models utilized to represent them, highlighting a limitation that diary instruments (and underlying models) of this type would have in supporting people to reflect upon and possibly change their mobility choices.
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.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
Cookie settings
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.