Learning Dimensional Descent planning for a highly-articulated robot arm
Paul Vernaza, Daniel D. Lee · 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011
We present an method for generating high-quality plans for a robot arm with many degrees of freedom based on Learning Dimensional Descent (LDD), a recently-developed algorithm for planning in high-dimensional spaces based on machine learning and optimization techniques. Unlike other approaches used to solve this problem, our method optimizes a well-defined objective and can be shown to generate optimal plans, in theory and practice, for a well-defined class of problems—those that possess low-dimensional cost structure. For the common case where such structure is only approximately present, LDD constitutes a powerful iterative optimization technique that makes non-homotopic path adjustments in each iteration, while still providing a guarantee of convergence to a local minimum of the objective. Experiments with a 7-DOF robot arm show that the method is able to find solutions in cluttered environments that are of a much higher quality than can be obtained with sampling-based planners and smoothing.