让机器人通过好奇心驱动想象，快速适应未知环境变化。
Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation
- 用神经网络与符号规划结合，让机器人在想象中试错探索
- 在动态环境中收敛速度更快，成功率显著高于现有方法
- 适合需要自主适应新场景的机器人系统研究者
在动态、不确定的开放世界中实现快速适应仍是机器人领域的重大挑战。传统任务与运动规划（TAMP）方法难以应对突发变化，适应过程数据效率低，且学习阶段未利用世界模型。本文提出一种混合规划与学习系统，融合两个模型：底层基于神经网络的模型学习随机转移，并通过内在好奇心模块（ICM）驱动探索；高层符号规划模型用操作符捕捉抽象转移，使智能体能在“想象空间”中规划并生成奖励机器。在引入序列新颖性的机器人操作领域评估中，该方法收敛更快，性能优于现有最先进混合方法。
原文摘要 · Abstract (English)
Adapting quickly to dynamic, uncertain environments-often called "open worlds"-remains a major challenge in robotics. Traditional Task and Motion Planning (TAMP) approaches struggle to cope with unforeseen changes, are data-inefficient when adapting, and do not leverage world models during learning. We address this issue with a hybrid planning and learning system that integrates two models: a low level neural network based model that learns stochastic transitions and drives exploration via an Intrinsic Curiosity Module (ICM), and a high level symbolic planning model that captures abstract transitions using operators, enabling the agent to plan in an "imaginary" space and generate reward machines. Our evaluation in a robotic manipulation domain with sequential novelty injections demonstrates that our approach converges faster and outperforms state-of-the-art hybrid methods.
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