All deadlines are in Pacific Standard Times (PST)
Extended Abstract Submission deadline: August 1, 2019, 6 pm August 20, 2019, 6 pm
Planning algorithms for control, also known as Motion Planning, has a long history ranging from methods with complete to probabilistic worst-case guarantees. However, despite having deep roots in artificial intelligence, these methods tend to be computationally inefficient in high-dimensional problems. On the other hand, machine learning advancements have led toward the systems that can perform complex decision-making by directly using the raw sensory information, thanks advancement in function approximation. This workshop aims to bring these two long-lived research communities under one forum to share insights towards building computationally tractable planning methods while retaining the theoretical guarantees.
Motion planning is the amalgamation of several vital components working together to generate a feasible plan which includes, but not limited to, sample generation, collision detection, nearest neighbor search, best edge selection, pruning, and rewiring. In this workshop, we aim to formalize the challenges and advantages of merging the machine learning methods with motion planning. We also seek to address how the advanced machine learning techniques can be leveraged to provide data-driven solutions to bottlenecks of motion planning. In general, the workshop will revolve around the following themes.
A list of topics addressed in the workshop:
We invite extended abstracts (minimum 2 pages) followed by camera-ready submission of accepted papers (no longer than 8 pages including references). All papers should follow the IEEE Conference Templates [Latex, MS Word]. Submissions can be original research, late-breaking results, or a literature review that fall under the scope of the workshop. All submissions should be made through the following link: https://cmt3.research.microsoft.com/LRPC2019.
All papers will be reviewed via single-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. The papers acceptance decision will be based on contribution, novelty, and overall content.
All accepted papers will have oral presentations. Each speaker will have 8 minutes for their talk and 2 mins for the Q&A session. The changeover between speakers will happen during the Q&A session. Please bring your laptop for the presentation. We are expecting to have a projector (16:9) with a VGA connector.
Waseda University
University of North Carolina
UC San Diego
TU Darmstadt
University of Michigan
Stanford University
Stanford University