時空間チューブ報酬を用いた信号時相論理仕様の全クラスに対する実用的強化学習
Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward
信号時相論理(STL)で表された複雑な高レベル仕様を満たすため、時空間チューブの幾何学的性質を利用した時間認識型強化学習フレームワークを提案し、入力制約を厳守しつつ履歴不要で効率的に連続制御方策を学習する。
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著者: Vaishnavi Jagabathula, P Sangeerth, Pushpak Jagtap
分類: cs.RO, eess.SY
原文アブストラクト
This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and strict actuator limits to satisfy complex high-level specifications. We denote these high-level specifications using Signal Temporal Logic (STL) and propose a novel time-aware Reinforcement Learning (RL) framework that leverages the geometric properties of Spatiotemporal Tubes (STTs). While traditional analytical STT controllers often struggle to enforce input constraints, and existing RL approaches rely on memory-intensive state history, our method natively overcomes both limitations. By mapping the logical and temporal complexities of the full class of STL into time-varying geometric boundaries, we directly constrain the multidimensional system state without relying on scalar robustness metrics. Augmenting the state space with time, we train a time-aware Soft Actor-Critic (SAC) agent using a continuous, geometry-aware reward function that eliminates the need to explicitly evaluate complex logical semantics during execution. The proposed framework offers a history-free, computationally efficient approach to learn continuous control policies that ensure robust satisfaction of specifications while strictly adhering to system input constraints.