<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">FINAL CALL FOR PAPERS</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Third Machine Learning in Planning and Control of Robot Motion Workshop at</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">IEEE International Conference on Robotics and Automation (ICRA) 2018</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Monday, 21-May-2018</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Brisbane, Australia</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; color: rgb(0, 105, 217);" class=""><span style="text-decoration: underline ; font-kerning: none" class=""><a href="http://www.cs.unm.edu/amprg/Workshops/MLPC18/index.html" class="">http://www.cs.unm.edu/amprg/Workshops/MLPC18/index.html</a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Paper submission deadline: *** March 21st, 2018 ***</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">=====================================================================</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">ABSTRACT:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Modern robots are expected to perform complex tasks in changing environments. Nonlinear dynamic, model uncertainty, and high-dimensional configuration spaces make planning and executing the motions required for these tasks is difficult. Recent success has been made through the integration of planning and control methods with tools from machine learning. For example, clustering, reinforcement learning, and intelligent heuristics have adaptively solved planning problems in complex spaces, have automatically identified appropriate trajectories for robots with complex dynamics, and have reduced the amount of time required for planning motions.</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">After the success of the First Workshop (<a href="http://www.cs.unm.edu/amprg/mlpc14Workshop" class=""><span style="-webkit-font-kerning: none;" class="">http://www.cs.unm.edu/amprg/mlpc14Workshop</span></a>) and Second Workshop (<a href="http://kormushev.com/MLPC-2015" class=""><span style="-webkit-font-kerning: none; color: rgb(102, 17, 204);" class="">http://kormushev.com/MLPC-2015</span></a>) in Machine Learning in the Planning and Control of Robot Motion at IROS 2014 in Chicago and IROS 2015 in Hamburg, it is the goal of this workshop to continue to explore methods and advancements afforded by the integration of machine learning for the planning and control of robot motion. The objectives of this workshop are to:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Develop a community of researchers working on machine learning methods in complementary fields of motion planning and controls</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Discuss current state of the art and future directions of intelligent motion planning and controls</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Provide for collaboration opportunities</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">MOTIVATION AND OBJECTIVES:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">To meet the objectives, the workshop will:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Include high-quality keynote talks by the leaders of the fields.</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Solicit extended abstracts and short papers (2-4 pages) contributions of the state of the art, and preliminary research results. We aim at accepting approximately 12 short papers and several extended abstracts.</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Organize the workshop with ample time for informal meeting and networking, including group lunch and dinner.</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Conclude the day with an interactive panel discussion and dialogue between the invited speakers, contributed authors, and audience on the next challenges in the field on machine learning for motion planning and control.</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">LIST OF TOPICS (included, but not limited to):</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Because machine learning methods are often heuristic, issues such as safety and performance are critical. Also, learning-based questions such as problem learnability, knowledge transfer among robots, knowledge generalization, long-term autonomy, task formulation, demonstration, role of simulation, and methods for feature selection define problem solvability. The topics include:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Task representation and classification</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Planning for complex and high dimensional environments</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Smart sampling techniques for motion planning</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Learning feature selection</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Methods for incorporating learning into planning</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Reinforcement learning for robotics and dynamical systems</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Transfer of learning and motion plans, knowledge and experience sharing among the agents</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Policy selection: exploration versus exploitation, methods for safe exploration</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Methods for creating motion plans that meet dynamical constraints</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Task planning and learning under uncertainty and disturbance</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Motion planning for system stability</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Adaptable heuristics for efficient motion plans</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Motion generalization - methods that learn subset of motion and produce plans with higher range of motions</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Motion planning for multi-agent systems and fleets</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">INTENDED AUDIENCE:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Motion planners with interests in learning and planning for changing agents, environment, or both</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Reinforcement learning and machine learning communities that develop novel learning methods for autonomous agents</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Multi-agent researchers</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Controls community focused on controlling physical systems</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">- Robotics community</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">=====================================================================</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">SOCIAL MEDIA:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; color: rgb(0, 105, 217);" class=""><span style="font-kerning: none; color: #222222" class="">Like us on Facebook: <a href="https://www.facebook.com/mlpcworkshop" class=""><span style="-webkit-font-kerning: none;" class="">https://www.facebook.com/mlpcworkshop</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; color: rgb(0, 105, 217);" class=""><span style="font-kerning: none; color: #222222" class="">Follow us on Twitter: <a href="https://twitter.com/MLPC18" class=""><span style="-webkit-font-kerning: none;" class="">https://twitter.com/MLPC18</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Please feel free to contact the workshop committee at mlpc18\AT\<a href="http://googlegroups.com/" class=""><span style="-webkit-font-kerning: none;" class="">googlegroups.com</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">=====================================================================</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">ORGANIZERS:</span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial; min-height: 18px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Aleksandra Faust, Google Brain, faust\AT\<a href="http://google.com/" class=""><span style="-webkit-font-kerning: none;" class="">google.com</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Tsz-Chiu Au, Ulsan National Institute of Science and Technology, chiu\AT\<a href="http://unist.ac.kr/" class=""><span style="-webkit-font-kerning: none;" class="">unist.ac.kr</span></a><span class="Apple-tab-span" style="white-space:pre"> </span></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Hao-Tien (Lewis) Chiang, University of New Mexico, lewispro\AT\<a href="http://unm.edu/" class=""><span style="-webkit-font-kerning: none;" class="">unm.edu</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">James Davidson, Google Brain, jcdavidson\AT\<a href="http://google.com/" class=""><span style="-webkit-font-kerning: none;" class="">google.com</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Hanna Kurniawati, University of Queensland, hannakur\AT\<a href="http://uq.edu.au/" class=""><span style="-webkit-font-kerning: none;" class="">uq.edu.au</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; font-family: Arial;" class=""><span style="font-kerning: none" class="">Lydia Tapia, University of New Mexico, tapia\AT\<a href="http://cs.unm.edu/" class=""><span style="-webkit-font-kerning: none;" class="">cs.unm.edu</span></a></span></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 19px;" class=""><span style="font-kerning: none" class=""></span><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><span style="font-kerning: none" class="">-- </span></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><span style="font-kerning: none" class="">You received this message because you are subscribed to the Google Groups "ICAPS Conference" group.</span></div><div style="margin: 0px; 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