Abstract
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Running tour generation for unknown environments
at
| Jutta Willamowski, Stéphane Clinchant, Christophe Legras, Sofia Michel, Shreepriya Shreepriya |
| HCII 2019, Orlando, USA, 26-31 July, 2019 |
| Download |
In this paper, we present an approach to generate pleasant running tours for unknown environments. We start from a first version, able to generate basic pleasant tours, but without explicitly catering for elevation. Based on observations on how runners appreciate elevation gain and steepness and on interviews with local runners, we identified corresponding additional needs. Indeed, runners usually have specific implicit or explicit objectives or limits with respect to the elevation gain they want to target or to avoid with a tour. In consequence, we extend our first approach. We expose the algorithm we have defined to address elevation constraints during tour generation and the underlying intuitions. An important differentiator with prior art is that we address elevation constraints during the tour generation phase and not a posteriori. This means that our approach is able to efficiently generate new tours that match the user’s constraints, while prior art rather finds matching tours by searching a posteriori among a set of available tours, and only finds matching ones if they are already present.
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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