这篇论文提出了一种无奖励持续学习框架，对于太空机器人适应硬件退化问题有重要意义，值得一读。
针对太空机器人极端环境中的硬件退化问题，提出一种无奖励持续学习框架，利用潜在状态世界模型进行在线适应。通过在多样化模拟中预训练模型代理，世界模型学习其潜在空间中的奖励结构预测器。在部署到硬件退化严重的环境中，仅通过无监督回滚更新世界模型的转换动态。通过训练策略以更新后的世界模型生成的想象轨迹，代理适应改变的动力而不需要新的奖励。在模拟行星穿越、轨道导航和精密组装任务中展示了该方法的有效性。
Reward-Free Continual Adaptation for Resilient Space Robots
Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a promising mechanism for online adaptation, it inherently requires access to a reward signal during deployment. However, precise reward computation in space is often infeasible due to the lack of external tracking systems and the overall complexity of the environment. To address the challenge of unobservable rewards, we introduce a reward-free continual learning framework that leverages latent-state world models. By pre-training a model-based agent across diverse simulations, the world model learns a robust predictor of the reward structure within its latent space. Upon deployment to an environment with severe hardware degradation, we freeze the observation encoder and reward predictor to update only the transition dynamics of the world model through unsupervised rollouts. By training the policy entirely on imagined trajectories generated by this updated world model, the agent adapts to altered dynamics without receiving new rewards. We demonstrate our approach across simulated planetary traversal, orbital navigation, and precision assembly tasks subjected to severe morphological failures.