STOMP: Stochastic trajectory optimization for motion planning

TLDR

The paper proposes a stochastic trajectory optimization framework for motion planning. The method generates noisy trajectories around an initial path, optimizes a combined obstacle‑smoothness cost without requiring gradients, and iteratively updates the trajectory, demonstrated in simulation and on a mobile manipulation system. Experiments show STOMP escapes local minima that gradient‑based planners such as CHOMP cannot.

Abstract

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.