In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from a semi-flexible array of densely packed bins). As a real-time perception-driven feedback controller, MPC is robust to the uncertainties in this environment. However, our experiment shows MPC cannot control a robot to complete a sequence of motions in a heavily occluded environment due to its myopic nature. It will benefit from adding a high-level policy that adaptively adjusts the optimization problem for MPC.
@article{arxiv.2211.15752,
title = {Hierarchical Control Strategy for Moving A Robot Manipulator Between Small Containers},
author = {Paolo Torrado and Boling Yang and Joshua Smith},
journal= {arXiv preprint arXiv:2211.15752},
year = {2022}
}