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Open access
Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. To achieve this, we use and extend MPC-Net, which is an Imitation Learning approach guided by Model Predictive Control (MPC). The strategy of MPC-Net differs from many other approaches since its objective is to minimize the control Hamiltonian, which derives from the principle of optimality. To represent the policies, we employ a mixture-of-experts network (MEN) and observe that the performance of a policy improves if each expert of a MEN specializes in controlling exactly one mode of a hybrid system, such as a walking robot. We introduce new loss functions for single- and multi-gait policies to achieve this kind of expert selection behavior. Moreover, we benchmark our algorithm against Behavioral Cloning and the original MPC implementation on various rough terrain scenarios. We validate our approach on hardware and show that a single learned policy can replace its teacher to control multiple gaits. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000476607Publication status
publishedExternal links
Book title
2021 IEEE International Conference on Robotics and Automation (ICRA)Pages / Article No.
Publisher
IEEEEvent
Subject
Imitation Learning; Legged Robots; Optimization and Optimal ControlOrganisational unit
09570 - Hutter, Marco / Hutter, Marco
Funding
166232 - Data-driven control approaches for advanced legged locomotion (SNF)
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
852044 - Learning Mobility for Real Legged Robots (EC)
Notes
RSLMore
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ETH Bibliography
yes
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