IROS2020LTAWS: IROS 2020 Workshop on Reliable Deployment of Machine Learning for Long-Term Autonomy Worshop of the IROS 2020 Conference (Online) Online, NV, United States, October 25, 2020 |
Conference website | http://longtermautonomy.eu |
Submission link | https://easychair.org/conferences/?conf=icra2020ltaws |
Submission deadline | October 1, 2020 |
IROS 2020 Workshop
Reliable Deployment of Machine Learning for Long-Term Autonomy
October 25th - Online
Website: longtermautonomy.eu
Conference website: https://www.iros2020.org/
============================================================
Important dates
October 1th Submission deadline
October 14th: Notification of acceptance
October 22nd: Camera-ready paper
============================================================
Format:
Authors are required to submit a 2 pages extended abstract or 4 pages short paper or 6 pages full paper as PDF in the standard IROS conference format.
Submissions:
Submissions will be judged based on relevance to the workshop topics, technical quality, and novelty. Authors of accepted papers are expected to give a lightning talk (2-3 minutes) and to present a poster at the workshop. A number of full papers will be selected for longer 10-15 video presentation.
https://easychair.org/conferences/?conf=icra2020ltaws
Overview & Topics
Achieving long-term autonomy by mobile robots means the ability to operate autonomously under no/or minimal supervision for days, weeks, months or even years. During these long periods, the environment where the robot operates can experience unpredictable gradual or/and radical changes. This fact adds an extra dimension to the fundamental problems in robotics such as perception, planning, navigation, SLAM and manipulation; and makes them more challenging.
One of the keys to achieving long-term autonomy is having reliable sub-components in the robotic operating system, including the machine learning-based ones. In this context, reliability means that the components can identify and recover from failures and prevent or reduce the likelihood of failures in general, which otherwise can terminate the mission of the robot or/and might cause severe danger.
This workshop focuses on the problem of long-term autonomy for mobile robots and the challenge of building a reliable machine learning components in the robotic system that can handle bad sensory data, shifts to abnormal operational conditions, misclassification and detections. We bring together experts in the serious deployment of robots in real-world applications to discuss the main challenges that face such robots and talk about their own experiences and the lessons they learnt after long-term deployments of their robots.
Topics of interest include, but are not limited to:
- Reasoning about environmental appearance and structural change.
- Lifelong learning and adaptation.
- Failure detection and recovery.
- Long-term mission planning and exploration
- Spatial representation for long-term mapping and localisation.
- State estimation in dynamic environments.
- Context-dependent decision making
- Verification of long-term autonomous systems
- Reliability, Dependability and Explainability of Machine Learning for robotics.
Organizers
-
Feras Dayoub, Australian Centre for Robotic Vision, QUT, AU
-
Tomas Krajnik, Czech Technical University in Prague
-
Niko Suenderhauf, Australian Centre for Robotic Vision, QUT, AU
-
Ayoung Kim, Korea Advanced Institute of Science and Technology
For more information and for submissions please contact feras.dayoub@qut.edu.au
This full-day workshop is supported by the IEEE Technical Committee on Autonomous Ground Vehicles and Intelligent Transportation Systems, (http://www.ieee-ras.org/autonomous-ground-vehicles-and-intelligent-transportation-systems)