做3D视觉和机器人探索的团队终于有了一个能跳出局部循环的解决方案——持久世界模型加情景记忆的组合直接解决了好奇心驱动的核心痛点，值得在复杂环境中一试。
该研究提出了一种名为 Recuriosity 的强化学习方法，解决好奇心驱动探索在3D环境中容易陷入局部循环的问题。核心创新在于使用在线3D重建作为持久的世界模型，并让智能体通过序列模型维护情景轨迹历史，从而避免重复访问已遗忘状态。仅基于好奇心训练，智能体在 HM3D 数据集上超越了基于强化学习的主动建图基线，并零样本泛化到 Gibson 和 AI 生成的世界。该方法还能高效适配下游任务，如摘苹果和图像目标导航，优于从头训练的基线。
Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration
Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence and episodic context. We show that effective curiosity requires a model of the world that is persistent and continuously updated, paired with an agent that maintains an episodic trajectory history to navigate toward novel regions. We achieve this using an online 3D reconstruction as a persistent model of the world, while the agent policy is parameterized as a sequence model over RGB observations to maintain episodic context. This design enables effective exploration during training while allowing the agent to navigate using solely RGB frames at deployment. Trained purely via curiosity on HM3D, our agent outperforms RL-based active mapping baselines and generalizes zero-shot to Gibson and AI-generated worlds. Our end-to-end policy enables efficient adaptation to downstream tasks, such as apple picking and image-goal navigation, outperforming from-scratch baselines. Please see video results at https://recuriosity.github.io/.