@inproceedings{92e8693234924444b72c3c79c4dfe54a,
title = "Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance",
abstract = "Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motions for humanoid robots. However, the effect of uncertainties on constraints satisfaction has only been studied using Robust MPC (RMPC) approaches, which account for the worst-case realization of bounded disturbances at each time instant. In this paper, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. We show that SMPC offers more flexibility to the user (or a high level decision maker) by tolerating small (user-defined) probabilities of constraint violation. Therefore, SMPC can be tuned to achieve a constraint satisfaction probability that is arbitrarily close to 100%, but without sacrificing performance as much as tube-based RMPC. We compare SMPC against RMPC in terms of robustness (constraint satisfaction) and performance (optimality). Our results highlight the benefits of SMPC and its interest for the robotics community as a powerful mathematical tool for dealing with uncertainties.",
author = "Ahmad Gazar and Majid Khadiv and {Del Prete}, Andrea and Ludovic Righetti",
note = "Publisher Copyright: {\textcopyright}2021 IEEE.; 20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020 ; Conference date: 19-07-2021 Through 21-07-2021",
year = "2021",
doi = "10.1109/HUMANOIDS47582.2021.9555783",
language = "English (US)",
series = "IEEE-RAS International Conference on Humanoid Robots",
publisher = "IEEE Computer Society",
pages = "61--68",
editor = "Tamim Asfour and Dongheui Lee and Mombaur Katja and Katsu Yamane and Kensuke Harada and Ludovic Righetti and Nikos Tsagarakis and Tomomichi Sugihara",
booktitle = "2020 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020",
}