Learning initial trajectory using sequence-to-sequence approach to warm start an optimization-based motion planner
Sankaranarayanan Natarajan
In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS-2021), 27.9.-01.10.2021, Prague/Virtual, o.A., Sep/2021.
Abstract
:
In recent years, optimization-based motion planners
have shown that they can provide a fast, smooth, and
locally optimal trajectory even for a higher dimension planning
problem. Their convergence rate depends on the given initial
trajectory. The proper selection of an initial trajectory is
crucially important: if it is not within the basin of attraction
of the optimum, it will take longer to convergence or even get
stuck in local minima. This paper presents a neural networkbased
initial trajectory predictor, which utilizes the power of the
sequence-to-sequence (Seq2Seq) learning method to predict a
good initial trajectory for an optimization-based motion planner
even in an unseen environment. The proposed model learns
the mapping between the tasks and the optimal trajectories
from a database. Given a start and a goal configuration of
a manipulator along with the environment information in the
form of a voxel grid, the proposed model predicts a good initial
trajectory, which was learned from previously seen situations.
The learned model is evaluated in a 6 degree of freedom
(DOF) manipulator planning in two different environments. The
results show that by using the predicted initial trajectory, there
is a significant improvement in the convergence rate and the
planning time of an optimization-based motion planner, even
in an unseen environment.