SE(3)多様体上のハミルトン型ニューラルODEによるダイナミクス学習と制御
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
剛体のダイナミクスをSE(3)多様体上のハミルトン型ニューラルODEで表現し、エネルギー保存を構造的に保証しつつ、エネルギー整形と減衰注入による安定化・軌道追従制御を実現した。
著者: Thai Duong, Nikolay Atanasov
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
