Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Abstract
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized images. We then propose Kalman Randomized-to-Canonical Model Predictive Control (KRC-MPC) as a zero-shot sim-to-real transferable visual MPC using KRC-model. The effectiveness of our method is evaluated through a valve rotation task by a robot hand in both simulation and the real world, and a block mating task in simulation. The experimental results show that KRC-MPC can be applied to various real domains and tasks in a zero-shot manner.
Keywords
Cite
@article{arxiv.2207.01840,
title = {Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation},
author = {Tomoya Yamanokuchi and Yuhwan Kwon and Yoshihisa Tsurumine and Eiji Uchibe and Jun Morimoto and Takamitsu Matsubara},
journal= {arXiv preprint arXiv:2207.01840},
year = {2022}
}
Comments
8 pages, Accepted by Robotics and Automation Letters