点群観測からのハミルトン力学学習による非ホロノミック移動ロボット制御
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
点群データから直接ロボットの力学を学習し、ハミルトン構造を組み込むことでデータ効率を高め、非ホロノミック移動ロボットの追従制御を実現した。
著者: Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal, Thai Duong, Nikolay Atanasov
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Reliable autonomous navigation requires adapting the control policy of a mobile robot in response to dynamics changes in different operational conditions. Hand-designed dynamics models may struggle to capture model variations due to a limited set of parameters. Data-driven dynamics learning approaches offer higher model capacity and better generalization but require large amounts of state-labeled data. This paper develops an approach for learning robot dynamics directly from point-cloud observations, removing the need and associated errors of state estimation, while embedding Hamiltonian structure in the dynamics model to improve data efficiency. We design an observation-space loss that relates motion prediction from the dynamics model with motion prediction from point-cloud registration to train a Hamiltonian neural ordinary differential equation. The learned Hamiltonian model enables the design of an energy-shaping model-based tracking controller for rigid-body robots. We demonstrate dynamics learning and tracking control on a real nonholonomic wheeled robot.
