Robotics: Science and Systems XVII

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Thai P Duong, Nikolay A Atanasov


Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models; however; may be insufficiently accurate; even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots; including ground; aerial; and underwater vehicles; are described in terms of their SE(3) pose and generalized velocity; and satisfy conservation of energy principles. This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body. In contrast to a black-box ODE network; our formulation guarantees total energy conservation by construction. We develop energy shaping and damping injection control for the learned; potentially under-actuated SE(3) Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various platforms; including pendulum; rigid-body; and quadrotor systems.



    AUTHOR    = {Thai P Duong AND Nikolay A Atanasov}, 
    TITLE     = {{Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.086}