Please post your questions and any discussion in this slack channel by June 10 for inclusion in our panel discussion with invited speakers.
Above, please sign up above to receive updates about the workshop and subscribe to the slack workspace. Due to COVID-19 we plan to have a free, virtual, asynchronous workshop with three track taking place primarily over June 1-12.
Virtual Conference Schedule
For accepted papers: June 1st, the papers and author presentations will be posted, along with a slack channel for each paper for a Q&A with the authors over the course of the conference.
For invited talks: June 8th, the invited talks will be posted.
For the panel: June 1st - 10th, a slack channel will be open to ask questions of the panelists about Machine Learning for Planning and Controls and from their talks. These questions will then be discussed virtually and posted on the workshop website.
Final Version and Presentation Instructions
The final version of accepted papers is due via Microsoft CMT at https://cmt3.research.microsoft.com/MLPC2020/ by May 27, 2020 AOE along with an optional video, e.g., a presentation, demonstration, or other visual figure (max 3 minutes). The papers and presentations will then be posted on this website.
Modern robots are increasingly expected to perform complex tasks in the real world. These tasks are complicated by high-dimensional state spaces, changing environments, nonlinear dynamics, and significant uncertainty throughout the robotic stack. The ability to plan and control robotic systems lies at the core of addressing these challenges. Recent successes in machine learning offer promising steps forward towards addressing these issues.
Following previous workshops on Machine Learning in the Planning and Control of Robot Motion (2014, 2015, 2018), this workshop seeks to continue to explore methods and directions for integrating machine learning with robotic planning and control. This workshop will feature talks from leading experts, panel discussions, solicited papers, and poster presentations in an effort to:
- Develop a community of researchers working on machine learning methods in complementary fields of motion planning and controls
- Discuss current state of the art and future directions of intelligent motion planning and controls
- Provide for collaboration opportunities
Call for Papers
Please submit all submissions in IEEE format via Microsoft CMT at https://cmt3.research.microsoft.com/MLPC2020/ by April 8, 2020 AOE (Extended from March 18, 2020 AOE).
We invite authors to submit via two tracks to the workshop: short papers or demos.
- Papers (up to 6 pages in the above format)- to be presented as posters with a selected few spotlight talks
- Demos / Interactive Exhibits (1 page extended abstract)- e.g., robot demonstrations, software demonstrations
The deadline for these submissions is April 8, 2020 AOE (Extended from March 18, 2020 AOE) with decisions announced May 11, 2020 (Extended from May 1). There will be no formal publication of workshop proceedings. However, the accepted papers will be made available online on the workshop website as non-archival reports to allow submissions to future conferences/journals.
Alan Kuntz (Univeristy of Utah), Anastasiia Varava (KTH), Angela Schoellig (University of Toronto), CAI Panpan (National University of Singapore), Edward Schmerling (Waymo Research), Hanna Kurniawati (ANU), Jens Kober (TU Delft), Jia Pan (Univerity of Hong Kong), Jon Scholz (Deepmind), Karol Hausman (Robotics at Google), Lewis Chiang (Waymo Research), Liangjun Zhang (Baidu Reseach), Lydia Kavraki (Rice), Marco Morales (Instituto Tecnologico Autonomo de Mexico), Maria Gini (University of Minnesota), Melanie Moses (UNM), Michael Yip (UC San Diego), Nancy Amato (Texas A&M University), Stefan Schaal (Google X), Sumeet Singh (Robotics at Google), Torsten Kroeger (Google X), Tsz-Chiu Au (Ulsan National Institute of Science and Technology), Vijay Reddi (Harvard), Xuesu Xiao (UT Austin)