AI for Long-Term Autonomy
A Special Issue organized by T.Duckett, L. Kunze, M. Hanheide, G. Sibley, and N. Hawes
- Kunze, N. Hawes, T. Duckett, and M. Hanheide, “Introduction to the Special Issue on AI for Long-Term Autonomy,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4431–4434, Oct. 2018, doi: 10.1109/LRA.2018.2870466.
- Kunze, N. Hawes, T. Duckett, M. Hanheide, and T. Krajnk, “Artificial intelligence for long-term robot autonomy: A survey,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4023–4030, Oct. 2018, doi: 10.1109/LRA.2018.2860628.
- D. Duchetto, A. Kucukyilmaz, L. Iocchi, and M. Hanheide, “Do not make the same mistakes again and again: Learning local recovery policies for navigation from human demonstrations,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4084–4091, Oct. 2018, doi: 10.1109/LRA.2018.2861080.
- P. Herrero, J. P. Fentanes, and M. Hanheide, “Getting to know your robot customers: Automated analysis of user identity and demographics for robots in the wild,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3733–3740, Oct. 2018, doi: 10.1109/LRA.2018.2856264.
- Chaudhary, K. Wada, X. Chen, K. Kimura, K. Okada, and M. Inaba, “Learning to segment generic handheld ob-
jects using class-agnostic deep comparison and segmentation network,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3844–3851, Oct. 2018, doi: 10.1109/LRA.2018.2856917.
- Han, S. E. Beleidy, H. Wang, C. Ye, and H. Zhang, “Learning of holism-landmark graph embedding for place recognition in long-term autonomy,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3669–3676, Oct. 2018, doi: 10.1109/LRA.2018.2856274.
- Sun, Z. Yan, A. Zaganidis, C. Zhao, and T. Duckett, “Recurrent-OctoMap: Learning state-based map refinement for long-term semantic mapping with 3-D-lidar data,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3749–3756, Oct. 2018, doi: 10.1109/LRA.2018.2856268.
- Bescos, J. M. Fcil, J. Civera, and J. Neira, “DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4076– 4083, Oct. 2018, doi: 10.1109/LRA.2018.2860039.
- Chen, L. Liu, I. Sa, Z. Ge, and M. Chli, “Learning context flexible attention model for long-term visual place recognition,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4015–4022, Oct. 2018, doi: 10.1109/LRA.2018.2859916.
Autonomous systems have a long history in the fields of Artificial Intelligence (AI) and Robotics. However, only through recent advances in technology has it been possible to create autonomous systems capable of operating in long-term, real-world scenarios. Examples include autonomous robots that operate outdoors on land, air, water, and space, and indoors in offices, care homes, and factories. Designing, developing, and maintaining intelligent autonomous systems that operate in real-world environments over long periods of time, i.e. weeks, months, or years, poses many challenges. The proposed Special Issue will focus on such challenges and on ways to overcome them using methods from AI.
The size of this community is estimated as several hundred researchers. The proposal follows the successful organization of the ICRA 2016 Workshop on AI for Long-Term Autonomy, and previous events held at major robotics conferences (e.g. ICRA Workshops on Long-term Autonomy, 2011-2014) and events on AI and Robotics (http://ai-robotics.wikispaces.com/events).
Motivation and Endorsements
The proposal of this Special Issue is motivated by the success of the Workshop “AI for Long-Term Autonomy”, organized at the ICRA’16 conference on 16 May 2016 (https://sites.google.com/site/icra2016ailta/). The quality of the speakers and number of attendees (ca 200) were very encouraging. In the absence of published proceedings of the Workshop, the important contributions presented there could be missed by a wider public. The proposed Special Issue would also encourage participation by other attendees of the workshop and the wider scientific community working on related issues in AI for long-running robotic systems.
The ICRA’16 Workshop on AI for Long-Term Autonomy was endorsed by the IEEE Technical Committee on Cognitive Robotics (please see endorsement letter in attachment).
- Special Issue Call Publication:November 8, 2017
- Special Issue Submission Opens: February 17, 2018
- Special Issue Submission Closes: February 24, 2018 (same as RAL/IROS)
- First Decision Communicated to Authors: May 21, 2018
- Final Decision Communicated to Authors: July 25, 2018
- Accepted RAL Papers appear on Xplore: August 23, 2018
The Special Issue will be associated with the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 1-5, 2018, Madrid, Spain and accepted articles will be presented at the conference. Authors of submissions commit that, if accepted by the IROS Program Committee, they will present the work at IROS.