到達可能性に基づく軌道安全層RTS：連続制御のための安全で高速な強化学習セーフティレイヤ
Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
ロボットの不確実なモデルから前方到達可能集合を事前計算し、強化学習エージェントの軌道選択を実行時に安全か判定・修正する安全層を提案。12次元のクアッドロータなど3つの非線形モデルで有効性を示した。
著者: Yifei Simon Shao, Chao Chen, Shreyas Kousik, Ram Vasudevan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks due to their ability to reason about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this paper proposes a Reachability-based Trajectory Safeguard (RTS), which leverages reachability analysis to ensure safety during training and operation. Given a known (but uncertain) model of a robot, RTS precomputes a Forward Reachable Set of the robot tracking a continuum of parameterized trajectories. At runtime, the RL agent selects from this continuum in a receding-horizon way to control the robot; the FRS is used to identify if the agent's choice is safe or not, and to adjust unsafe choices. The efficacy of this method is illustrated on three nonlinear robot models, including a 12-D quadrotor drone, in simulation and in comparison with state-of-the-art safe motion planning methods.
