Abstract
Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer from catastrophic forgetting. Hybrid planning and RL approaches have shown some promise in handling environmental changes but lack efficiency in accommodation speed. To address this limitation, we propose an enhanced hybrid system with a nested hierarchical action abstraction that can utilize previously acquired skills to effectively tackle unexpected novelties. We show that it can adapt faster and generalize better compared to state-of-the-art RL and hybrid approaches, significantly improving robustness when multiple environmental changes occur at the same time.
Originalsprache | Englisch |
---|---|
Titel | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
Seiten | 508-514 |
DOIs | |
Publikationsstatus | Veröffentlicht - 8 Aug. 2024 |
Veranstaltung | IEEE International Conference on Robotics and Automation - Yokohama, Japan Dauer: 13 Mai 2024 → 17 Mai 2024 https://2024.ieee-icra.org/ |
Publikationsreihe
Name | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
---|
Konferenz
Konferenz | IEEE International Conference on Robotics and Automation |
---|---|
Kurztitel | ICRA |
Land/Gebiet | Japan |
Stadt | Yokohama |
Zeitraum | 13/05/24 → 17/05/24 |
Internetadresse |
Research Field
- Complex Dynamical Systems