俊敏ロボティクス：最適制御・強化学習・微分可能シミュレーション
Agile Robotics: Optimal Control, Reinforcement Learning, and Differentiable Simulation
最適制御・強化学習・微分可能シミュレーションを組み合わせ、ロボットの俊敏性を高めつつ外乱に頑健な制御系を設計する方法を論じた論文。
著者: Yunlong Song, Davide Scaramuzza
分類: cs.RO
原文アブストラクト
Control systems are at the core of every real-world robot. They are deployed in an ever-increasing number of applications, ranging from autonomous racing and search-and-rescue missions to industrial inspections and space exploration. To achieve peak performance, certain tasks require pushing the robot to its maximum agility. How can we design control algorithms that enhance the agility of autonomous robots and maintain robustness against unforeseen disturbances? This paper addresses this question by leveraging fundamental principles in optimal control, reinforcement learning, and differentiable simulation.
