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Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning

2019

Conference Paper

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Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to intractable problems. Furthermore, since the models used in TO have always some level of abstraction, it can be hard to find a realistic set of uncertainties in the model space. In this paper we leverage a sample-efficient learning technique (Bayesian optimization) to robustify TO for humanoid locomotion. The main idea is to use data from full-body simulations to make the TO stage robust by tuning the cost weights. To this end, we split the TO problem into two phases. The first phase solves a convex optimization problem for generating center of mass (CoM) trajectories based on simplified linear dynamics. The second stage employs iterative Linear-Quadratic Gaussian (iLQG) as a whole-body controller to generate full body control inputs. Then we use Bayesian optimization to find the cost weights to use in the first stage that yields robust performance in the simulation/experiment, in the presence of different disturbance/uncertainties. The results show that the proposed approach is able to generate robust motions for different sets of disturbances and uncertainties.

Author(s): Mohammad Hasan Yeganegi and Majid Khadiv and S. Ali A. Moosavian and Jia-Jie Zhu and Andrea Del Prete and Ludovic Righetti
Book Title: Proceedings International Conference on Humanoid Robots
Year: 2019
Publisher: IEEE

Department(s): Movement Generation and Control
Bibtex Type: Conference Paper (conference)
Paper Type: Conference

Event Name: 2019 IEEE-RAS International Conference on Humanoid Robots
Event Place: Toronto, Canada

Digital: True
State: Published
Talk Type: Lecture

Links: https://arxiv.org/abs/1907.04616

BibTex

@conference{Majid Khadiv,
  title = {Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning},
  author = {Yeganegi, Mohammad Hasan and Khadiv, Majid and Moosavian, S. Ali A. and Zhu, Jia-Jie and Prete, Andrea Del and Righetti, Ludovic},
  booktitle = {Proceedings International Conference on Humanoid Robots},
  publisher = {IEEE},
  year = {2019}
}