適応的強化学習とモデル予測制御の切替による安全な人間-ロボット協調ナビゲーション
Adaptive Reinforcement and Model Predictive Control Switching for Safe Human-Robot Cooperative Navigation
強化学習とモデル予測制御を状況に応じて切り替えるハイブリッド手法を提案し、混雑環境での移動ロボットの人間誘導ナビゲーションの安全性と成功率を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Ning Liu, Sen Shen, Zheng Li, Matthew D'Souza, Jen Jen Chung, Thomas Braunl
分類: cs.RO
原文アブストラクト
This paper addresses the challenge of human-guided navigation for mobile collaborative robots under simultaneous proximity regulation and safety constraints. We introduce Adaptive Reinforcement and Model Predictive Control Switching (ARMS), a hybrid learning-control framework that integrates a reinforcement learning follower trained with Proximal Policy Optimization (PPO) and an analytical one-step Model Predictive Control (MPC) formulated as a quadratic program safety filter. To enable robust perception under partial observability and non-stationary human motion, ARMS employs a decoupled sensing architecture with a Long Short-Term Memory (LSTM) temporal encoder for the human-robot relative state and a spatial encoder for 360-degree LiDAR scans. The core contribution is a learned adaptive neural switcher that performs context-aware soft action fusion between the two controllers, favoring conservative, constraint-aware QP-based control in low-risk regions while progressively shifting control authority to the learned follower in highly cluttered or constrained scenarios where maneuverability is critical, and reverting to the follower action when the QP becomes infeasible. Extensive evaluations against Pure Pursuit, Dynamic Window Approach (DWA), and an RL-only baseline demonstrate that ARMS achieves an 82.5 percent success rate in highly cluttered environments, outperforming DWA and RL-only approaches by 7.1 percent and 3.1 percent, respectively, while reducing average computational latency by 33 percent to 5.2 milliseconds compared to a multi-step MPC baseline. Additional simulation transfer in Gazebo and initial real-world deployment results further indicate the practicality and robustness of ARMS for safe and efficient human-robot collaboration. Source code and a demonstration video are available at https://github.com/21ning/ARMS.git.
