リー群上のポート・ハミルトニアン神経ODEネットワークによるロボット動力学の学習と制御
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
リー群上のポート・ハミルトニアン形式をニューラルODEに組み込み、エネルギー保存則と摩擦・抗力などの散逸効果を考慮したロボット動力学モデルを学習し、安定化と軌道追従のための統一的な制御則を提案した。
著者: Thai Duong, Abdullah Altawaitan, Jason Stanley, Nikolay Atanasov
分類: cs.RO, 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 are described in terms of their generalized coordinates on a matrix Lie group, e.g. on $SE(3)$ for ground, aerial, and underwater vehicles, and generalized velocity, and satisfy conservation of energy principles. This paper proposes a port-Hamiltonian formulation over a Lie group of the structure of a neural ordinary differential equation (ODE) network to approximate the robot dynamics. In contrast to a black-box ODE network, our formulation embeds energy conservation principle and Lie group's constraints in the dynamics model and explicitly accounts for energy-dissipation effect such as friction and drag forces in the dynamics model. We develop energy shaping and damping injection control for the learned, potentially under-actuated Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various robot platforms.
