ICRA 2011 Paper Abstract


Paper ThA105.3

Kalakrishnan, Mrinal (University of Southern California), Chitta, Sachin (Willow Garage Inc.), Theodorou, Evangelos (University of Southern California), Pastor, Peter (University of Southern California), Schaal, Stefan (University of Southern California)

STOMP: Stochastic Trajectory Optimization for Motion Planning

Scheduled for presentation during the Regular Sessions "Manipulation Planning I" (ThA105), Thursday, May 12, 2011, 08:50−09:05, Room 3G

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 19, 2019

Keywords Motion and Path Planning, Manipulation Planning


We 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 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 methods like CHOMP can get stuck in.



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