NavWAM：用于目标条件视觉导航的导航世界动作模型
NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation
AI总结：
提出NavWAM，一种扩散变换器策略，通过联合学习未来观测、目标进度值和动作块，将导航世界模型预测直接转化为可执行动作，在离线基准和真实机器人部署中优于基于规划的世界模型基线。
摘要：
目标条件视觉导航要求机器人在部分可观测性下行动，通过预测其运动将如何改变未来的自我中心视图以及这种变化是否使其更接近目标。导航世界模型提供了这种视觉预见，但它们仍然是预测模块，需要外部规划器将预测的未来转化为闭环控制。我们提出导航世界动作模型（NavWAM），一种扩散变换器策略，通过将未来观测、目标进度值和动作块表示为共享的潜在序列，将导航世界模型预测转化为可执行动作。通过联合学习未来预测与决定闭环行为的动作和价值目标，NavWAM使视觉预见可直接用于机器人控制。我们通过模拟预训练和真实机器人适应构建NavWAM，并在图像目标导航任务上将其与基于规划的世界模型和代表性直接导航策略进行评估。在离线基准和闭环真实机器人部署中，NavWAM在使用默认策略模式（无CEM式动作搜索）的情况下，在我们的评估中优于基于规划的世界模型基线。项目页面：此 https URL
英文摘要：
Goal-conditioned visual navigation requires a robot to act under partial observability by anticipating how its motion will change the future egocentric view and whether that change brings it closer to the goal. Navigation world models provide such visual foresight, but they remain prediction modules that require an external planner to convert predicted futures into closed-loop control. We propose Navigation World Action Model (NavWAM), a diffusion-transformer policy that turns navigation world-model prediction into executable action by representing future observations, goal-progress values, and action chunks in a shared latent sequence. By learning future prediction jointly with the action and value targets that determine closed-loop behavior, NavWAM makes visual foresight directly usable for robot control. We build NavWAM through simulation pretraining and real-robot adaptation, and evaluate it on image-goal navigation against planning-based world models and a representative direct navigation policy. Across offline benchmarks and closed-loop real-robot deployment, NavWAM improves over planning-based world-model baselines in our evaluations while using the default policy mode without CEM-style action search. Project page: https://dachii-azm.github.io/navwam/
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。
