Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
This addresses the need for safe and stable control in robotics by providing a physics-informed learning approach that generalizes across various platforms.
The paper tackled the problem of learning accurate robot dynamics models for control by proposing a Hamiltonian-based neural ODE network on the SE(3) manifold, which guarantees energy conservation and enables stabilization and trajectory tracking for systems like pendulums and quadrotors.
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.