Brisbane, May 2018
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.
After the success of the First and Second Workshop in Machine Learning in the Planning and Control of Robot Motion at IROS 2014 in Chicago (http://www.cs.unm.edu/amprg/mlpc14Workshop/) and IROS 2015 in Hamburge(http://kormushev.com/MLPC-2015/), 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:
- 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