MPC-Net: A First Principles Guided Policy Search (Presentation)

ICRA 2020

MPC-Net: A First Principles Guided Policy Search (Presentation)

Sep 18, 2020
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Paper presentation at ICRA 2020: MPC-Net - An imitation learning method for robotics at the border of classical control theory and deep learning. Corresponding Publication: J. Carius, F. Farshidian and M. Hutter, "MPC-Net: A First Principles Guided Policy Search," in IEEE Robotics and Automation Letters 5 (2), 2897-2904 Arxiv PDF: https://arxiv.org/pdf/1909.05197.pdf Official PDF: https://doi.org/10.1109/LRA.2020.2974653 Accompanying paper video: https://www.youtube.com/watch?v=VI7wt5PCJ14 Paper abstract: We present an Imitation Learning approach for the control of dynamical systems with a known model. Our policy search method is guided by solutions from MPC. Typical policy search methods of this kind minimize a distance metric between the guiding demonstrations and the learned policy. Our loss function, however, corresponds to the minimization of the control Hamiltonian, which derives from the principle of optimality. Therefore, our algorithm directly attempts to solve the optimality conditions with a parameterized class of control laws. Additionally, the proposed loss function explicitly encodes the constraints of the optimal control problem and we provide numerical evidence that its minimization achieves improved constraint satisfaction. We train a mixture-of-expert neural network architecture for controlling a quadrupedal robot and show that this policy structure is well suited for such multimodal systems. The learned policy can successfully stabilize different gaits on the real walking robot from less than 10 min of demonstration data.

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