Reliable Deployment of Machine Learning for Long-Term Autonomy
ICRA 2020 - Workshop
Due to the current unfortunate circumstances of the pandemic, this workshop will be cancelled and will not run as part of the ICRA'20 virtual conference. We have asked to be included in the IROS'20 program for later this year.
If you submitted a paper to our workshop, as a thank you for considering us, we are going to review your papers and give feedback.
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 onse. 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 reliabile machine learning components in the robotic system that can handle bad sensory data, shifts to abnormal operational conditions, misclassification and detections.
We invite several renowned experts in the field who will highlight the main challenges these robots face and talk about their own experiences and the lessons they learnt during long-term deployments of their robots. We also call for papers that address the long-term autonomy problem and in particular the topics below:
- 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.
- Nick Hawes, University of Oxford
- Michael Milford, Queensland University of Technology (QUT)
- Tom Duckett, University of Lincoln
- Tim Barfoot, University of Toronto
- Zhi Yan, Université de technologie de Belfort Montbéliard (UTBM)
- Ben Upcroft, Oxbotica (Industry)
- Stefan Williams, University of Sydney
April 14th Submission deadline
April 24th: Notification of acceptance
May 1st: Camera ready paper
June 4th: Workshop (full-day)
to come soon
Feras Dayoub, Australian Centre for Robotic Vision, Queensland University of Technology, AU
Tomas Krajnik, Czech Technical University in Prague
Niko Suenderhauf, Australian Centre for Robotic Vision, Queensland University of Technology, AU
Ayoung Kim, Korea Advanced Institute of Science and Technology