ロボット学習と制御のための構造化力学モデル
Structured Mechanical Models for Robot Learning and Control
ロボットの力学をデータから学習する際に、物理的構造を組み込んだ「構造化力学モデル」を提案し、ブラックボックスなニューラルネットより少ないデータで汎化し、信頼性の高いモデルベース制御ができることを示した。
著者: Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge. We introduce Structured Mechanical Models, a flexible model class for mechanical systems that are data-efficient, easily amenable to prior knowledge, and easily usable with model-based control techniques. The goal of this work is to demonstrate the benefits of using Structured Mechanical Models in lieu of black-box neural networks when modeling robot dynamics. We demonstrate that they generalize better from limited data and yield more reliable model-based controllers on a variety of simulated robotic domains.
