OffRIPP: オフライン強化学習による情報獲得型経路計画
OffRIPP: Offline RL-based Informative Path Planning
環境とのリアルタイムな相互作用なしに、事前収集データのみから強化学習で情報獲得型経路計画を学習するフレームワークを提案し、シミュレーションと実機実験で有効性を示した。
著者: Srikar Babu Gadipudi, Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia Sycara, Woojun Kim
分類: cs.RO, cs.AI
原文アブストラクト
Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution -- key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach.
