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Reference TypeConference Paper
Author(s)Kalakrishnan, M.;Chitta, S.;Theodorou, E.;Pastor, P.;Schaal, S.
TitleSTOMP: Stochastic trajectory optimization for motion planning
Journal/Conference/Book TitleRobotics and Automation (ICRA), 2011 IEEE International Conference on
Keywordsreinforcement learning, optimization, optimal motion planning
AbstractWe present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a dual-arm mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based optimizers like CHOMP can get stuck in.
Place PublishedShanghai, China, May 9-13
Short TitleSTOMP: Stochastic trajectory optimization for motion planning

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