OpenNWM: A Generalist Navigation World Model with Latent Action Pretraining
Overview
This research presents OpenNWM, a generalist navigation world model that leverages latent action pretraining to enhance generalization across real-world environments. The authors introduce NavAnywhere, a large-scale dataset comprising 17.5 million visual observations from 15 heterogeneous sources, capturing navigation by ground robots, humans, and drones across five broad scene types. By learning universal latent actions from action-free videos and aligning them with physical motions through post-training, OpenNWM achieves improved visual prediction and navigation performance on both in-domain and out-of-domain benchmarks.
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Reported quantity · visual observations from sources: 15 other · Basis not reported
“vations from 15 heterogeneous sources. It captures navigation by ground robots, humans, and drones, with diverse viewpoints and motion patterns across five broad scene types: residential areas, public and commercial spaces, urban settings, parks, and natural landscapes. Experiments demonstrate improved visual prediction on in-domain and out-of-domain benchmarks, together with improved navigation performance. These re”
Exact source · revision 1Source owner not reported
Reported quantity · sources: 15 other · Basis not reported
“vations from 15 heterogeneous sources. It captures navigation by ground robots, humans, and drones, with diverse viewpoints and motion patterns across five broad scene types: residential areas, public and commercial spaces, urban settings, parks, and natural landscapes. Experiments demonstrate improved visual prediction on in-domain and out-of-domain benchmarks, together with improved navigation performance. These re”
Exact source · revision 1Source owner not reported
Reported quantity · scene types: 5 other · Basis not reported
“vations from 15 heterogeneous sources. It captures navigation by ground robots, humans, and drones, with diverse viewpoints and motion patterns across five broad scene types: residential areas, public and commercial spaces, urban settings, parks, and natural landscapes. Experiments demonstrate improved visual prediction on in-domain and out-of-domain benchmarks, together with improved navigation performance. These re”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalOpenNWM: A Generalist Navigation World Model with Latent Action Pretraining
This research presents OpenNWM, a generalist navigation world model that leverages latent action pretraining to enhance generalization across real-world environments. The authors introduce NavAnywhere, a large-scale dataset comprising 17.5 million visual observations from 15 heterogeneous sources, capturing navigation by ground robots, humans, and drones across five broad scene types. By learning universal latent actions from action-free videos and aligning them with physical motions through post-training, OpenNWM achieves improved visual prediction and navigation performance on both in-domain and out-of-domain benchmarks.
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