ITPNet: Initial Trajectory Prediction Network - A deep learning framework for predicting initial trajectories to warm start optimization-based motion planners

Abstract Motion planning for robot manipulators in a cluttered environment is one of the most researched areas in the field of robotics.For high degrees of freedom (DOF) robotic systems, optimization-based motion planners are often preferred for trajectory planning, as they are computationally efficient and provide a smooth, locally optimal solution.To converge to an optimal solution, an optimization-based motion planner needs a good initial trajectory. Finding an initial trajectory, which is not far away from the basin of attraction of the optimum is not a trivial task. In this work, we propose an Initial Trajectory Prediction Network (ITPNet), a deep neural network framework for predicting an initial trajectory to warm start optimization-based motion planners.Given a planning task in the form of task and environment features, the ITPNet predicts the best initial trajectory for warm starting an optimization-based motion planner. Two different task features: joint and Cartesian features, and three types of environment features extracted using: Principal Component Analysis (PCA), Variational Autoencoder (VAE), and Signed Spatial Distance (SSD) techniques are compared.The learned models are evaluated on an upper-torso humanoid system in two different scenarios. The results show that the model, using the Cartesian task and the SSD-based environment features, efficiently learns the mapping between the planning tasks and the optimal trajectories. Warm-starting the planner with the predicted initial trajectory, even for an unseen environment results in a higher success rate and requires fewer iterations.