時相論理と強化学習を統合したマルチタスク跳躍ロボットナビゲーション
A Unified Approach to Multi-task Legged Navigation: Temporal Logic Meets Reinforcement Learning
線形時相論理で与えられた必須の目標達成タスクと、報酬関数で表される任意の探索タスクを同時にこなす跳躍ロボットのナビゲーション手法を提案し、動力学の不確かさに対処する統合制御ポリシーを導出した。
著者: Jesse Jiang, Samuel Coogan, Ye Zhao
分類: cs.RO
原文アブストラクト
This study examines the problem of hopping robot navigation planning to achieve simultaneous goal-directed and environment exploration tasks. We consider a scenario in which the robot has mandatory goal-directed tasks defined using Linear Temporal Logic (LTL) specifications as well as optional exploration tasks represented using a reward function. Additionally, there exists uncertainty in the robot dynamics which results in motion perturbation. We first propose an abstraction of 3D hopping robot dynamics which enables high-level planning and a neural-network-based optimization for low-level control. We then introduce a Multi-task Product IMDP (MT-PIMDP) model of the system and tasks. We propose a unified control policy synthesis algorithm which enables both task-directed goal-reaching behaviors as well as task-agnostic exploration to learn perturbations and reward. We provide a formal proof of the trade-off induced by prioritizing either LTL or RL actions. We demonstrate our methods with simulation case studies in a 2D world navigation environment.
