Journal ArticleGuided Policy Search for Stabilizing Contact-rich Motion PlansDagher, Christopher; Silva, Chandika; Satici, Aykut C; Poonawala, Hasan ALearning policies for contact-rich manipulation is a challenging problem due to the presence of multiple contact modes with different dynamics, which complicates state and action exploration. Contact-rich motion planning uses simplified dynamics to reduce the search space dimension, but the found plans are then difficult to execute under the true object-manipulator dynamics. This paper presents an algorithm for learning controllers based on guided policy search, where motion plans based on simplified dynamics define rewards and sampling distributions for policy gradient-based learning. We demonstrate that our guided policy search method improves the ability to learn manipulation controllers, through a task involving pushing a box over a step.Elsevier2024-01-0110595917IFAC-PapersOnLine58281019 to 10242405-8963https://doi.org/10.1016/j.ifacol.2025.01.1302330794National Science Foundation