让机器人快速应对未知变化，结合符号与神经网络学习。
Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Robotics
- 用符号目标学习+世界模型探索，实现快速适应。
- 在机械臂和自动驾驶中收敛更快、样本效率更高。
- 适合需要实时应变的开放世界机器人任务。
在开放世界环境中应对未预见的新情况，仍是自主系统的一大挑战。尽管混合规划与强化学习方法展现出潜力，但常面临样本效率低、适应慢和灾难性遗忘等问题。本文提出一种融合层次抽象、任务与运动规划（TAMP）及强化学习的神经符号框架，实现机器人在环境变化下的快速适应。该架构结合符号化的目标导向学习与基于世界模型的探索机制，显著提升适应速度。在机器人抓取与自动驾驶任务中验证，相比现有先进混合方法，本方法具备更快收敛速度、更高样本效率和更强鲁棒性，展现出实际部署潜力。
原文摘要 · Abstract (English)
Adapting to unforeseen novelties in open-world environments remains a major challenge for autonomous systems. While hybrid planning and reinforcement learning (RL) approaches show promise, they often suffer from sample inefficiency, slow adaptation, and catastrophic forgetting. We present a neuro-symbolic framework integrating hierarchical abstractions, task and motion planning (TAMP), and reinforcement learning to enable rapid adaptation in robotics. Our architecture combines symbolic goal-oriented learning and world model-based exploration to facilitate rapid adaptation to environmental changes. Validated in robotic manipulation and autonomous driving, our approach achieves faster convergence, improved sample efficiency, and superior robustness over state-of-the-art hybrid methods, demonstrating its potential for real-world deployment.
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