Learning based motion planning
Title: Learning based motion planning
DNr: Berzelius-2023-25
Project Type: LiU Berzelius
Principal Investigator: Bernhard Wullt <bernhard.wullt@it.uu.se>
Affiliation: Uppsala universitet
Duration: 2023-02-27 – 2023-09-01
Classification: 10201


We want to apply machine learning to solve motion planning problems for manipulators in time-varying environment. Our approach is to use imitation learning, meaning that we want to mimic an expert motion planner that knows how to solve the problem. The policy and work-space encoding network is parameterized by a deep neural network, hence requires GPU:s for training in a scalable way. We have previously evaluated our approach in a 2D environments, but now we aim to increase the difficulty as a next step and train our networks for a 3D environment, which increases the required amount of compute needed to train these models. Once we have a planner that can solve our simulated world, we want to deploy this trained planner in a real world to evaluate its performance.