OSVI-WM：通过世界模型引导轨迹生成实现单次视觉模仿
OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation
AI总结：
OSVI-WM通过世界模型引导轨迹生成，实现对未见任务的单次视觉模仿学习，提升机器人执行复杂任务的能力。
摘要：
视觉模仿学习使机器人代理能够通过观察专家示范视频来获得技能。在单次设置中，代理在观察一个专家示范后生成策略，而无需额外微调。现有方法通常在相同任务集上训练和评估，仅改变物体配置，难以泛化到具有不同语义或结构要求的未见任务。虽然一些最近的方法试图解决这个问题，但它们在困难的测试任务上表现较低，这些任务尽管在视觉上与某些训练任务相似，但存在不同的上下文并需要不同的响应。此外，大多数现有方法缺乏对环境动态的显式模型，限制了它们对未来状态的推理能力。为了解决这些限制，我们提出了一种新的单次视觉模仿学习框架，通过世界模型引导轨迹生成。给定一个专家示范视频和代理的初始观察，我们的方法利用学习的世界模型来预测一系列潜在状态和动作。此潜在轨迹随后被解码为物理路径点，以指导代理的执行。我们的方法在两个模拟基准和三个现实世界机器人平台上进行评估，结果在某些情况下比先前方法提高了超过30%。代码可在https://github.com/raktimgg/osvi-wm上获得。
英文摘要：
Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typically train and evaluate on the same set of tasks, varying only object configurations, and struggle to generalize to unseen tasks with different semantic or structural requirements. While some recent methods attempt to address this, they exhibit low success rates on hard test tasks that, despite being visually similar to some training tasks, differ in context and require distinct responses. Additionally, most existing methods lack an explicit model of environment dynamics, limiting their ability to reason about future states. To address these limitations, we propose a novel framework for one-shot visual imitation learning via world-model-guided trajectory generation. Given an expert demonstration video and the agent's initial observation, our method leverages a learned world model to predict a sequence of latent states and actions. This latent trajectory is then decoded into physical waypoints that guide the agent's execution. Our method is evaluated on two simulated benchmarks and three real-world robotic platforms, where it consistently outperforms prior approaches, with over 30% improvement in some cases. The code is available at https://github.com/raktimgg/osvi-wm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
