
Sign up to save your podcasts
Or
Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。
今天的主题是:Navigation World ModelsSummary
This research introduces a Navigation World Model (NWM), a novel video generation model that predicts future visual observations for navigation. Employing a Conditional Diffusion Transformer (CDiT), NWM is trained on a massive dataset of human and robotic navigation videos, reaching 1 billion parameters. The model excels at planning navigation trajectories in known environments, either independently or by ranking trajectories from existing policies, and even generates imagined trajectories in unfamiliar environments from a single image. Experiments demonstrate state-of-the-art performance in visual navigation tasks, including the ability to incorporate navigation constraints during planning. Limitations include mode collapse in unseen environments and challenges with complex temporal dynamics.
原文链接:https://arxiv.org/abs/2412.03572
解读链接:https://www.jiqizhixin.com/articles/2024-12-07-4
Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。
今天的主题是:Navigation World ModelsSummary
This research introduces a Navigation World Model (NWM), a novel video generation model that predicts future visual observations for navigation. Employing a Conditional Diffusion Transformer (CDiT), NWM is trained on a massive dataset of human and robotic navigation videos, reaching 1 billion parameters. The model excels at planning navigation trajectories in known environments, either independently or by ranking trajectories from existing policies, and even generates imagined trajectories in unfamiliar environments from a single image. Experiments demonstrate state-of-the-art performance in visual navigation tasks, including the ability to incorporate navigation constraints during planning. Limitations include mode collapse in unseen environments and challenges with complex temporal dynamics.
原文链接:https://arxiv.org/abs/2412.03572
解读链接:https://www.jiqizhixin.com/articles/2024-12-07-4